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BInD: Bond and Interaction-generating Diffusion Model for Multi-objective Structure-based Drug Design (2405.16861v2)

Published 27 May 2024 in q-bio.BM, cs.LG, and physics.bio-ph

Abstract: A remarkable advance in geometric deep generative models with accumulated structural data enables structure-based drug design (SBDD) with target protein information only. However, most existing models struggle to address multi-objectives simultaneously while performing well only in their specialized tasks. Here, we present BInD, a diffusion model with knowledge-based guidance for multi-objective SBDD. BInD is designed to co-generate molecules and their interactions with a target protein to consider all key objectives equally well, including target-specific interactions, molecular properties, and local geometry. Comprehensive evaluations show that BInD achieves robust performance for all objectives while outperforming or matching state-of-the-art methods for each. Finally, we propose a train-free optimization method empowered by retrieving target-specific interactions, highlighting the role of non-covalent interactions in achieving higher selectivity and binding affinities to a target protein.

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References (51)
  1. Accurate structure prediction of biomolecular interactions with alphafold 3. Nature, pp.  1–3, 2024.
  2. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023.
  3. Renderdiffusion: Image diffusion for 3d reconstruction, inpainting and generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.  12608–12618, 2023.
  4. Anderson, A. C. The process of structure-based drug design. Chemistry & biology, 10(9):787–797, 2003.
  5. The structure of an mdm2–nutlin-3a complex solved by the use of a validated mdm2 surface-entropy reduction mutant. Acta Crystallographica Section D: Biological Crystallography, 69(8):1358–1366, 2013.
  6. Structured denoising diffusion models in discrete state-spaces. Advances in Neural Information Processing Systems, 34:17981–17993, 2021.
  7. Design and synthesis of aminopyrimidinyl pyrazole analogs as plk1 inhibitors using hybrid 3d-qsar and molecular docking. Pharmaceuticals, 15(10):1170, 2022.
  8. Quantifying the chemical beauty of drugs. Nature chemistry, 4(2):90–98, 2012.
  9. Chène, P. Inhibiting the p53–mdm2 interaction: an important target for cancer therapy. Nature reviews cancer, 3(2):102–109, 2003.
  10. A systematic analysis of atomic protein–ligand interactions in the pdb. Medchemcomm, 8(10):1970–1981, 2017.
  11. DeLano, W. L. et al. Pymol: An open-source molecular graphics tool. CCP4 Newsl. Protein Crystallogr, 40(1):82–92, 2002.
  12. Poseedit: Enhanced ligand binding mode communication by interactive 2d diagrams. Journal of Computer-Aided Molecular Design, 37(10):491–503, 2023.
  13. Povme 2.0: an enhanced tool for determining pocket shape and volume characteristics. Journal of chemical theory and computation, 10(11):5047–5056, 2014.
  14. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. Journal of cheminformatics, 1:1–11, 2009.
  15. Generation of 3d molecules in pockets via a language model. Nature Machine Intelligence, pp.  1–12, 2024.
  16. Three-dimensional convolutional neural networks and a cross-docked data set for structure-based drug design. Journal of chemical information and modeling, 60(9):4200–4215, 2020.
  17. 3d equivariant diffusion for target-aware molecule generation and affinity prediction. arXiv preprint arXiv:2303.03543, 2023.
  18. Decompdiff: diffusion models with decomposed priors for structure-based drug design. arXiv preprint arXiv:2403.07902, 2024.
  19. Posecheck: Generative models for 3d structure-based drug design produce unrealistic poses. In NeurIPS 2023 Generative AI and Biology (GenBio) Workshop, 2023.
  20. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840–6851, 2020.
  21. Highly accurate protein structure prediction with alphafold. Nature, 596(7873):583–589, 2021.
  22. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
  23. Fragment-based and structure-guided discovery and optimization of rho kinase inhibitors. Journal of medicinal chemistry, 55(5):2474–2478, 2012.
  24. Diffbp: Generative diffusion of 3d molecules for target protein binding. arXiv preprint arXiv:2211.11214, 2022.
  25. Generating 3d molecules for target protein binding. arXiv preprint arXiv:2204.09410, 2022.
  26. Dynamicbind: predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model. Nature Communications, 15(1):1071, 2024.
  27. Repaint: Inpainting using denoising diffusion probabilistic models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.  11461–11471, 2022.
  28. A 3d generative model for structure-based drug design. Advances in Neural Information Processing Systems, 34:6229–6239, 2021.
  29. A geometric deep learning approach to predict binding conformations of bioactive molecules. Nature Machine Intelligence, 3(12):1033–1039, 2021.
  30. Pignet: a physics-informed deep learning model toward generalized drug–target interaction predictions. Chemical Science, 13(13):3661–3673, 2022.
  31. Pignet2: a versatile deep learning-based protein–ligand interaction prediction model for binding affinity scoring and virtual screening. Digital Discovery, 2024.
  32. Open babel: An open chemical toolbox. Journal of cheminformatics, 3:1–14, 2011.
  33. Pocket2mol: Efficient molecular sampling based on 3d protein pockets. In International Conference on Machine Learning, pp.  17644–17655. PMLR, 2022.
  34. Moldiff: addressing the atom-bond inconsistency problem in 3d molecule diffusion generation. arXiv preprint arXiv:2305.07508, 2023.
  35. Kgdiff: towards explainable target-aware molecule generation with knowledge guidance. Briefings in Bioinformatics, 25(1):bbad435, 2024.
  36. Sparse training of discrete diffusion models for graph generation. arXiv preprint arXiv:2311.02142, 2023.
  37. Generating 3d molecules conditional on receptor binding sites with deep generative models. Chemical science, 13(9):2701–2713, 2022.
  38. Zero-shot text-to-image generation. In International conference on machine learning, pp.  8821–8831. Pmlr, 2021.
  39. Plip: fully automated protein–ligand interaction profiler. Nucleic acids research, 43(W1):W443–W447, 2015.
  40. E (n) equivariant graph neural networks. In International conference on machine learning, pp.  9323–9332. PMLR, 2021.
  41. Structure-based drug design with equivariant diffusion models. arXiv preprint arXiv:2210.13695, 2022.
  42. Pharmaconet: Accelerating large-scale virtual screening by deep pharmacophore modeling. In NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development, 2023.
  43. Boosting protein–ligand binding pose prediction and virtual screening based on residue–atom distance likelihood potential and graph transformer. Journal of Medicinal Chemistry, 65(15):10691–10706, 2022.
  44. Structure-based approach for the discovery of pyrrolo [3, 2-d] pyrimidine-based egfr t790m/l858r mutant inhibitors. ACS medicinal chemistry letters, 4(2):201–205, 2013.
  45. Autodock vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry, 31(2):455–461, 2010.
  46. On memorization in probabilistic deep generative models. Advances in Neural Information Processing Systems, 34:27916–27928, 2021.
  47. 3d-qsar in drug design-a review. Current topics in medicinal chemistry, 10(1):95–115, 2010.
  48. Midi: Mixed graph and 3d denoising diffusion for molecule generation. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp.  560–576. Springer, 2023.
  49. Guided diffusion for molecular generation with interaction prompt. Briefings in Bioinformatics, 25(3):bbae174, 2024.
  50. Specific noncovalent interactions at protein-ligand interface: implications for rational drug design. Current medicinal chemistry, 19(2):226–238, 2012.
  51. 3d molecular generative framework for interaction-guided drug design. Nature Communications, 15(1):2688, 2024.

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