Transferable Graph Neural Fingerprint Models for Quick Response to Future Bio-Threats (2308.01921v3)
Abstract: Fast screening of drug molecules based on the ligand binding affinity is an important step in the drug discovery pipeline. Graph neural fingerprint is a promising method for developing molecular docking surrogates with high throughput and great fidelity. In this study, we built a COVID-19 drug docking dataset of about 300,000 drug candidates on 23 coronavirus protein targets. With this dataset, we trained graph neural fingerprint docking models for high-throughput virtual COVID-19 drug screening. The graph neural fingerprint models yield high prediction accuracy on docking scores with the mean squared error lower than $0.21$ kcal/mol for most of the docking targets, showing significant improvement over conventional circular fingerprint methods. To make the neural fingerprints transferable for unknown targets, we also propose a transferable graph neural fingerprint method trained on multiple targets. With comparable accuracy to target-specific graph neural fingerprint models, the transferable model exhibits superb training and data efficiency. We highlight that the impact of this study extends beyond COVID-19 dataset, as our approach for fast virtual ligand screening can be easily adapted and integrated into a general machine learning-accelerated pipeline to battle future bio-threats.
- Wei Chen (1293 papers)
- Yihui Ren (30 papers)
- Ai Kagawa (2 papers)
- Matthew R. Carbone (25 papers)
- Samuel Yen-Chi Chen (64 papers)
- Xiaohui Qu (7 papers)
- Shinjae Yoo (83 papers)
- Austin Clyde (16 papers)
- Arvind Ramanathan (31 papers)
- Rick L. Stevens (11 papers)
- Hubertus J. J. van Dam (3 papers)
- Deyu Lu (24 papers)