2000 character limit reached
Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis (2311.10776v5)
Published 16 Nov 2023 in cs.IR and cs.AI
Abstract: Recent AI research plots a promising future of automatic chemical reactions within the chemistry society. This study proposes Chemist-X, a transformative AI agent that automates the reaction condition recommendation (RCR) task in chemical synthesis with retrieval-augmented generation (RAG) technology. To emulate expert chemists' strategies when solving RCR tasks, Chemist-X utilizes advanced RAG schemes to interrogate online molecular databases and distill critical data from the latest literature database. Further, the agent leverages state-of-the-art computer-aided design (CAD) tools with a LLM supervised programming interface. With the ability to utilize updated chemical knowledge and CAD tools, our agent significantly outperforms conventional synthesis AIs confined to the fixed knowledge within its training data. Chemist-X considerably reduces chemists' workload and allows them to focus on more fundamental and creative problems, thereby bringing closer computational techniques and chemical research and making a remarkable leap toward harnessing AI's full capabilities in scientific discovery.
- Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R.K., Kumar, P.: Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Molecular Diversity 25(3), 1315–1360 (2021) Williams et al. [2021] Williams, W.L., Zeng, L., Gensch, T., Sigman, M.S., Doyle, A.G., Anslyn, E.V.: The evolution of data-driven modeling in organic chemistry. ACS central science 7(10), 1622–1637 (2021) Hammett [1937] Hammett, L.P.: The effect of structure upon the reactions of organic compounds. benzene derivatives. Journal of the American Chemical Society 59(1), 96–103 (1937) Hansch and Fujita [1964] Hansch, C., Fujita, T.: p-σ𝜎\sigmaitalic_σ-π𝜋\piitalic_π analysis. a method for the correlation of biological activity and chemical structure. Journal of the American Chemical Society 86(8), 1616–1626 (1964) Corey and Wipke [1969] Corey, E.J., Wipke, W.T.: Computer-assisted design of complex organic syntheses: Pathways for molecular synthesis can be devised with a computer and equipment for graphical communication. Science 166(3902), 178–192 (1969) Fortunato et al. [2020] Fortunato, M.E., Coley, C.W., Barnes, B.C., Jensen, K.F.: Data augmentation and pretraining for template-based retrosynthetic prediction in computer-aided synthesis planning. Journal of chemical information and modeling 60(7), 3398–3407 (2020) Ahneman et al. [2018] Ahneman, D.T., Estrada, J.G., Lin, S., Dreher, S.D., Doyle, A.G.: Predicting reaction performance in c–n cross-coupling using machine learning. Science 360(6385), 186–190 (2018) Saebi et al. [2023] Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Williams, W.L., Zeng, L., Gensch, T., Sigman, M.S., Doyle, A.G., Anslyn, E.V.: The evolution of data-driven modeling in organic chemistry. ACS central science 7(10), 1622–1637 (2021) Hammett [1937] Hammett, L.P.: The effect of structure upon the reactions of organic compounds. benzene derivatives. Journal of the American Chemical Society 59(1), 96–103 (1937) Hansch and Fujita [1964] Hansch, C., Fujita, T.: p-σ𝜎\sigmaitalic_σ-π𝜋\piitalic_π analysis. a method for the correlation of biological activity and chemical structure. Journal of the American Chemical Society 86(8), 1616–1626 (1964) Corey and Wipke [1969] Corey, E.J., Wipke, W.T.: Computer-assisted design of complex organic syntheses: Pathways for molecular synthesis can be devised with a computer and equipment for graphical communication. Science 166(3902), 178–192 (1969) Fortunato et al. [2020] Fortunato, M.E., Coley, C.W., Barnes, B.C., Jensen, K.F.: Data augmentation and pretraining for template-based retrosynthetic prediction in computer-aided synthesis planning. Journal of chemical information and modeling 60(7), 3398–3407 (2020) Ahneman et al. [2018] Ahneman, D.T., Estrada, J.G., Lin, S., Dreher, S.D., Doyle, A.G.: Predicting reaction performance in c–n cross-coupling using machine learning. Science 360(6385), 186–190 (2018) Saebi et al. [2023] Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Hammett, L.P.: The effect of structure upon the reactions of organic compounds. benzene derivatives. Journal of the American Chemical Society 59(1), 96–103 (1937) Hansch and Fujita [1964] Hansch, C., Fujita, T.: p-σ𝜎\sigmaitalic_σ-π𝜋\piitalic_π analysis. a method for the correlation of biological activity and chemical structure. Journal of the American Chemical Society 86(8), 1616–1626 (1964) Corey and Wipke [1969] Corey, E.J., Wipke, W.T.: Computer-assisted design of complex organic syntheses: Pathways for molecular synthesis can be devised with a computer and equipment for graphical communication. Science 166(3902), 178–192 (1969) Fortunato et al. [2020] Fortunato, M.E., Coley, C.W., Barnes, B.C., Jensen, K.F.: Data augmentation and pretraining for template-based retrosynthetic prediction in computer-aided synthesis planning. Journal of chemical information and modeling 60(7), 3398–3407 (2020) Ahneman et al. [2018] Ahneman, D.T., Estrada, J.G., Lin, S., Dreher, S.D., Doyle, A.G.: Predicting reaction performance in c–n cross-coupling using machine learning. Science 360(6385), 186–190 (2018) Saebi et al. [2023] Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Hansch, C., Fujita, T.: p-σ𝜎\sigmaitalic_σ-π𝜋\piitalic_π analysis. a method for the correlation of biological activity and chemical structure. Journal of the American Chemical Society 86(8), 1616–1626 (1964) Corey and Wipke [1969] Corey, E.J., Wipke, W.T.: Computer-assisted design of complex organic syntheses: Pathways for molecular synthesis can be devised with a computer and equipment for graphical communication. Science 166(3902), 178–192 (1969) Fortunato et al. [2020] Fortunato, M.E., Coley, C.W., Barnes, B.C., Jensen, K.F.: Data augmentation and pretraining for template-based retrosynthetic prediction in computer-aided synthesis planning. Journal of chemical information and modeling 60(7), 3398–3407 (2020) Ahneman et al. [2018] Ahneman, D.T., Estrada, J.G., Lin, S., Dreher, S.D., Doyle, A.G.: Predicting reaction performance in c–n cross-coupling using machine learning. Science 360(6385), 186–190 (2018) Saebi et al. [2023] Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Corey, E.J., Wipke, W.T.: Computer-assisted design of complex organic syntheses: Pathways for molecular synthesis can be devised with a computer and equipment for graphical communication. Science 166(3902), 178–192 (1969) Fortunato et al. [2020] Fortunato, M.E., Coley, C.W., Barnes, B.C., Jensen, K.F.: Data augmentation and pretraining for template-based retrosynthetic prediction in computer-aided synthesis planning. Journal of chemical information and modeling 60(7), 3398–3407 (2020) Ahneman et al. [2018] Ahneman, D.T., Estrada, J.G., Lin, S., Dreher, S.D., Doyle, A.G.: Predicting reaction performance in c–n cross-coupling using machine learning. Science 360(6385), 186–190 (2018) Saebi et al. [2023] Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Fortunato, M.E., Coley, C.W., Barnes, B.C., Jensen, K.F.: Data augmentation and pretraining for template-based retrosynthetic prediction in computer-aided synthesis planning. Journal of chemical information and modeling 60(7), 3398–3407 (2020) Ahneman et al. [2018] Ahneman, D.T., Estrada, J.G., Lin, S., Dreher, S.D., Doyle, A.G.: Predicting reaction performance in c–n cross-coupling using machine learning. Science 360(6385), 186–190 (2018) Saebi et al. [2023] Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Ahneman, D.T., Estrada, J.G., Lin, S., Dreher, S.D., Doyle, A.G.: Predicting reaction performance in c–n cross-coupling using machine learning. Science 360(6385), 186–190 (2018) Saebi et al. [2023] Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018)
- Williams, W.L., Zeng, L., Gensch, T., Sigman, M.S., Doyle, A.G., Anslyn, E.V.: The evolution of data-driven modeling in organic chemistry. ACS central science 7(10), 1622–1637 (2021) Hammett [1937] Hammett, L.P.: The effect of structure upon the reactions of organic compounds. benzene derivatives. Journal of the American Chemical Society 59(1), 96–103 (1937) Hansch and Fujita [1964] Hansch, C., Fujita, T.: p-σ𝜎\sigmaitalic_σ-π𝜋\piitalic_π analysis. a method for the correlation of biological activity and chemical structure. Journal of the American Chemical Society 86(8), 1616–1626 (1964) Corey and Wipke [1969] Corey, E.J., Wipke, W.T.: Computer-assisted design of complex organic syntheses: Pathways for molecular synthesis can be devised with a computer and equipment for graphical communication. Science 166(3902), 178–192 (1969) Fortunato et al. [2020] Fortunato, M.E., Coley, C.W., Barnes, B.C., Jensen, K.F.: Data augmentation and pretraining for template-based retrosynthetic prediction in computer-aided synthesis planning. Journal of chemical information and modeling 60(7), 3398–3407 (2020) Ahneman et al. [2018] Ahneman, D.T., Estrada, J.G., Lin, S., Dreher, S.D., Doyle, A.G.: Predicting reaction performance in c–n cross-coupling using machine learning. Science 360(6385), 186–190 (2018) Saebi et al. [2023] Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Hammett, L.P.: The effect of structure upon the reactions of organic compounds. benzene derivatives. Journal of the American Chemical Society 59(1), 96–103 (1937) Hansch and Fujita [1964] Hansch, C., Fujita, T.: p-σ𝜎\sigmaitalic_σ-π𝜋\piitalic_π analysis. a method for the correlation of biological activity and chemical structure. Journal of the American Chemical Society 86(8), 1616–1626 (1964) Corey and Wipke [1969] Corey, E.J., Wipke, W.T.: Computer-assisted design of complex organic syntheses: Pathways for molecular synthesis can be devised with a computer and equipment for graphical communication. Science 166(3902), 178–192 (1969) Fortunato et al. [2020] Fortunato, M.E., Coley, C.W., Barnes, B.C., Jensen, K.F.: Data augmentation and pretraining for template-based retrosynthetic prediction in computer-aided synthesis planning. Journal of chemical information and modeling 60(7), 3398–3407 (2020) Ahneman et al. [2018] Ahneman, D.T., Estrada, J.G., Lin, S., Dreher, S.D., Doyle, A.G.: Predicting reaction performance in c–n cross-coupling using machine learning. Science 360(6385), 186–190 (2018) Saebi et al. [2023] Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Hansch, C., Fujita, T.: p-σ𝜎\sigmaitalic_σ-π𝜋\piitalic_π analysis. a method for the correlation of biological activity and chemical structure. Journal of the American Chemical Society 86(8), 1616–1626 (1964) Corey and Wipke [1969] Corey, E.J., Wipke, W.T.: Computer-assisted design of complex organic syntheses: Pathways for molecular synthesis can be devised with a computer and equipment for graphical communication. Science 166(3902), 178–192 (1969) Fortunato et al. [2020] Fortunato, M.E., Coley, C.W., Barnes, B.C., Jensen, K.F.: Data augmentation and pretraining for template-based retrosynthetic prediction in computer-aided synthesis planning. Journal of chemical information and modeling 60(7), 3398–3407 (2020) Ahneman et al. [2018] Ahneman, D.T., Estrada, J.G., Lin, S., Dreher, S.D., Doyle, A.G.: Predicting reaction performance in c–n cross-coupling using machine learning. Science 360(6385), 186–190 (2018) Saebi et al. [2023] Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Corey, E.J., Wipke, W.T.: Computer-assisted design of complex organic syntheses: Pathways for molecular synthesis can be devised with a computer and equipment for graphical communication. Science 166(3902), 178–192 (1969) Fortunato et al. [2020] Fortunato, M.E., Coley, C.W., Barnes, B.C., Jensen, K.F.: Data augmentation and pretraining for template-based retrosynthetic prediction in computer-aided synthesis planning. Journal of chemical information and modeling 60(7), 3398–3407 (2020) Ahneman et al. [2018] Ahneman, D.T., Estrada, J.G., Lin, S., Dreher, S.D., Doyle, A.G.: Predicting reaction performance in c–n cross-coupling using machine learning. Science 360(6385), 186–190 (2018) Saebi et al. [2023] Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Fortunato, M.E., Coley, C.W., Barnes, B.C., Jensen, K.F.: Data augmentation and pretraining for template-based retrosynthetic prediction in computer-aided synthesis planning. Journal of chemical information and modeling 60(7), 3398–3407 (2020) Ahneman et al. [2018] Ahneman, D.T., Estrada, J.G., Lin, S., Dreher, S.D., Doyle, A.G.: Predicting reaction performance in c–n cross-coupling using machine learning. Science 360(6385), 186–190 (2018) Saebi et al. [2023] Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Ahneman, D.T., Estrada, J.G., Lin, S., Dreher, S.D., Doyle, A.G.: Predicting reaction performance in c–n cross-coupling using machine learning. Science 360(6385), 186–190 (2018) Saebi et al. [2023] Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018)
- Hammett, L.P.: The effect of structure upon the reactions of organic compounds. benzene derivatives. Journal of the American Chemical Society 59(1), 96–103 (1937) Hansch and Fujita [1964] Hansch, C., Fujita, T.: p-σ𝜎\sigmaitalic_σ-π𝜋\piitalic_π analysis. a method for the correlation of biological activity and chemical structure. Journal of the American Chemical Society 86(8), 1616–1626 (1964) Corey and Wipke [1969] Corey, E.J., Wipke, W.T.: Computer-assisted design of complex organic syntheses: Pathways for molecular synthesis can be devised with a computer and equipment for graphical communication. Science 166(3902), 178–192 (1969) Fortunato et al. [2020] Fortunato, M.E., Coley, C.W., Barnes, B.C., Jensen, K.F.: Data augmentation and pretraining for template-based retrosynthetic prediction in computer-aided synthesis planning. Journal of chemical information and modeling 60(7), 3398–3407 (2020) Ahneman et al. [2018] Ahneman, D.T., Estrada, J.G., Lin, S., Dreher, S.D., Doyle, A.G.: Predicting reaction performance in c–n cross-coupling using machine learning. Science 360(6385), 186–190 (2018) Saebi et al. [2023] Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Hansch, C., Fujita, T.: p-σ𝜎\sigmaitalic_σ-π𝜋\piitalic_π analysis. a method for the correlation of biological activity and chemical structure. Journal of the American Chemical Society 86(8), 1616–1626 (1964) Corey and Wipke [1969] Corey, E.J., Wipke, W.T.: Computer-assisted design of complex organic syntheses: Pathways for molecular synthesis can be devised with a computer and equipment for graphical communication. Science 166(3902), 178–192 (1969) Fortunato et al. [2020] Fortunato, M.E., Coley, C.W., Barnes, B.C., Jensen, K.F.: Data augmentation and pretraining for template-based retrosynthetic prediction in computer-aided synthesis planning. Journal of chemical information and modeling 60(7), 3398–3407 (2020) Ahneman et al. [2018] Ahneman, D.T., Estrada, J.G., Lin, S., Dreher, S.D., Doyle, A.G.: Predicting reaction performance in c–n cross-coupling using machine learning. Science 360(6385), 186–190 (2018) Saebi et al. [2023] Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Corey, E.J., Wipke, W.T.: Computer-assisted design of complex organic syntheses: Pathways for molecular synthesis can be devised with a computer and equipment for graphical communication. Science 166(3902), 178–192 (1969) Fortunato et al. [2020] Fortunato, M.E., Coley, C.W., Barnes, B.C., Jensen, K.F.: Data augmentation and pretraining for template-based retrosynthetic prediction in computer-aided synthesis planning. Journal of chemical information and modeling 60(7), 3398–3407 (2020) Ahneman et al. [2018] Ahneman, D.T., Estrada, J.G., Lin, S., Dreher, S.D., Doyle, A.G.: Predicting reaction performance in c–n cross-coupling using machine learning. Science 360(6385), 186–190 (2018) Saebi et al. [2023] Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Fortunato, M.E., Coley, C.W., Barnes, B.C., Jensen, K.F.: Data augmentation and pretraining for template-based retrosynthetic prediction in computer-aided synthesis planning. Journal of chemical information and modeling 60(7), 3398–3407 (2020) Ahneman et al. [2018] Ahneman, D.T., Estrada, J.G., Lin, S., Dreher, S.D., Doyle, A.G.: Predicting reaction performance in c–n cross-coupling using machine learning. Science 360(6385), 186–190 (2018) Saebi et al. [2023] Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Ahneman, D.T., Estrada, J.G., Lin, S., Dreher, S.D., Doyle, A.G.: Predicting reaction performance in c–n cross-coupling using machine learning. Science 360(6385), 186–190 (2018) Saebi et al. [2023] Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018)
- Hansch, C., Fujita, T.: p-σ𝜎\sigmaitalic_σ-π𝜋\piitalic_π analysis. a method for the correlation of biological activity and chemical structure. Journal of the American Chemical Society 86(8), 1616–1626 (1964) Corey and Wipke [1969] Corey, E.J., Wipke, W.T.: Computer-assisted design of complex organic syntheses: Pathways for molecular synthesis can be devised with a computer and equipment for graphical communication. Science 166(3902), 178–192 (1969) Fortunato et al. [2020] Fortunato, M.E., Coley, C.W., Barnes, B.C., Jensen, K.F.: Data augmentation and pretraining for template-based retrosynthetic prediction in computer-aided synthesis planning. Journal of chemical information and modeling 60(7), 3398–3407 (2020) Ahneman et al. [2018] Ahneman, D.T., Estrada, J.G., Lin, S., Dreher, S.D., Doyle, A.G.: Predicting reaction performance in c–n cross-coupling using machine learning. Science 360(6385), 186–190 (2018) Saebi et al. [2023] Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Corey, E.J., Wipke, W.T.: Computer-assisted design of complex organic syntheses: Pathways for molecular synthesis can be devised with a computer and equipment for graphical communication. Science 166(3902), 178–192 (1969) Fortunato et al. [2020] Fortunato, M.E., Coley, C.W., Barnes, B.C., Jensen, K.F.: Data augmentation and pretraining for template-based retrosynthetic prediction in computer-aided synthesis planning. Journal of chemical information and modeling 60(7), 3398–3407 (2020) Ahneman et al. [2018] Ahneman, D.T., Estrada, J.G., Lin, S., Dreher, S.D., Doyle, A.G.: Predicting reaction performance in c–n cross-coupling using machine learning. Science 360(6385), 186–190 (2018) Saebi et al. [2023] Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Fortunato, M.E., Coley, C.W., Barnes, B.C., Jensen, K.F.: Data augmentation and pretraining for template-based retrosynthetic prediction in computer-aided synthesis planning. Journal of chemical information and modeling 60(7), 3398–3407 (2020) Ahneman et al. [2018] Ahneman, D.T., Estrada, J.G., Lin, S., Dreher, S.D., Doyle, A.G.: Predicting reaction performance in c–n cross-coupling using machine learning. Science 360(6385), 186–190 (2018) Saebi et al. [2023] Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Ahneman, D.T., Estrada, J.G., Lin, S., Dreher, S.D., Doyle, A.G.: Predicting reaction performance in c–n cross-coupling using machine learning. Science 360(6385), 186–190 (2018) Saebi et al. [2023] Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018)
- Corey, E.J., Wipke, W.T.: Computer-assisted design of complex organic syntheses: Pathways for molecular synthesis can be devised with a computer and equipment for graphical communication. Science 166(3902), 178–192 (1969) Fortunato et al. [2020] Fortunato, M.E., Coley, C.W., Barnes, B.C., Jensen, K.F.: Data augmentation and pretraining for template-based retrosynthetic prediction in computer-aided synthesis planning. Journal of chemical information and modeling 60(7), 3398–3407 (2020) Ahneman et al. [2018] Ahneman, D.T., Estrada, J.G., Lin, S., Dreher, S.D., Doyle, A.G.: Predicting reaction performance in c–n cross-coupling using machine learning. Science 360(6385), 186–190 (2018) Saebi et al. [2023] Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Fortunato, M.E., Coley, C.W., Barnes, B.C., Jensen, K.F.: Data augmentation and pretraining for template-based retrosynthetic prediction in computer-aided synthesis planning. Journal of chemical information and modeling 60(7), 3398–3407 (2020) Ahneman et al. [2018] Ahneman, D.T., Estrada, J.G., Lin, S., Dreher, S.D., Doyle, A.G.: Predicting reaction performance in c–n cross-coupling using machine learning. Science 360(6385), 186–190 (2018) Saebi et al. [2023] Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Ahneman, D.T., Estrada, J.G., Lin, S., Dreher, S.D., Doyle, A.G.: Predicting reaction performance in c–n cross-coupling using machine learning. Science 360(6385), 186–190 (2018) Saebi et al. [2023] Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018)
- Fortunato, M.E., Coley, C.W., Barnes, B.C., Jensen, K.F.: Data augmentation and pretraining for template-based retrosynthetic prediction in computer-aided synthesis planning. Journal of chemical information and modeling 60(7), 3398–3407 (2020) Ahneman et al. [2018] Ahneman, D.T., Estrada, J.G., Lin, S., Dreher, S.D., Doyle, A.G.: Predicting reaction performance in c–n cross-coupling using machine learning. Science 360(6385), 186–190 (2018) Saebi et al. [2023] Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Ahneman, D.T., Estrada, J.G., Lin, S., Dreher, S.D., Doyle, A.G.: Predicting reaction performance in c–n cross-coupling using machine learning. Science 360(6385), 186–190 (2018) Saebi et al. [2023] Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018)
- Ahneman, D.T., Estrada, J.G., Lin, S., Dreher, S.D., Doyle, A.G.: Predicting reaction performance in c–n cross-coupling using machine learning. Science 360(6385), 186–190 (2018) Saebi et al. [2023] Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018)
- Saebi, M., Nan, B., Herr, J.E., Wahlers, J., Guo, Z., Zurański, A.M., Kogej, T., Norrby, P.-O., Doyle, A.G., Chawla, N.V., et al.: On the use of real-world datasets for reaction yield prediction. Chemical Science 14(19), 4997–5005 (2023) Shields et al. [2021] Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018)
- Shields, B.J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J.I.M., Janey, J.M., Adams, R.P., Doyle, A.G.: Bayesian reaction optimization as a tool for chemical synthesis. Nature 590(7844), 89–96 (2021) Touvron et al. [2023] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018)
- Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al.: Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023) Du et al. [2023] Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018)
- Du, Y., Liew, S.C., Chen, K., Shao, Y.: The power of large language models for wireless communication system development: A case study on FPGA platforms. arXiv preprint arXiv:2307.07319 (2023) Brown et al. [2020] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018)
- Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) Du et al. [2023] Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018)
- Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023) Zhang et al. [2022] Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018)
- Zhang, B., Zhang, X., Du, W., Song, Z., Zhang, G., Zhang, G., Wang, Y., Chen, X., Jiang, J., Luo, Y.: Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proceedings of the National Academy of Sciences 119(41), 2212711119 (2022) Yang et al. [2021] Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018)
- Yang, Z., Chakraborty, M., White, A.D.: Predicting chemical shifts with graph neural networks. Chemical science 12(32), 10802–10809 (2021) St. John et al. [2020] St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018)
- St. John, P.C., Guan, Y., Kim, Y., Kim, S., Paton, R.S.: Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature communications 11(1), 2328 (2020) Chen et al. [2020] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018)
- Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018)
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018)
- Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Biau and Scornet [2016] Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018)
- Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016) Chen et al. [2023] Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018)
- Chen, K., Chen, G., Li, J., Huang, Y., Wang, E., Hou, T., Heng, P.-A.: MetaRF: attention-based random forest for reaction yield prediction with a few trails. Journal of Cheminformatics 15(1), 1–12 (2023) Chen and Guestrin [2016] Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018)
- Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016) Chen et al. [2015] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018)
- Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015) Probst et al. [2022] Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018)
- Probst, D., Schwaller, P., Reymond, J.-L.: Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital discovery 1(2), 91–97 (2022) Moriwaki et al. [2018] Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018) Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018)
- Moriwaki, H., Tian, Y.-S., Kawashita, N., Takagi, T.: Mordred: a molecular descriptor calculator. Journal of cheminformatics 10(1), 1–14 (2018)
- Kexin Chen (23 papers)
- Junyou Li (13 papers)
- Kunyi Wang (3 papers)
- Yuyang Du (14 papers)
- Jiahui Yu (65 papers)
- Jiamin Lu (4 papers)
- Lanqing Li (21 papers)
- Jiezhong Qiu (29 papers)
- Qun Fang (2 papers)
- Pheng Ann Heng (24 papers)
- Guangyong Chen (55 papers)
- Jianzhang Pan (1 paper)
- Yi Huang (162 papers)