Transferable Graph Neural Fingerprint Models for Quick Response to Future Bio-Threats
This work provides a machine learning-accelerated pipeline for virtual ligand screening that can be adapted to combat future bio-threats, though it is incremental as it builds on existing graph neural fingerprint methods.
The researchers tackled the problem of fast screening of drug molecules for COVID-19 by developing graph neural fingerprint models trained on a dataset of 300,000 drug candidates across 23 coronavirus protein targets, achieving mean squared error lower than 0.21 kcal/mol for most targets and outperforming conventional circular fingerprint methods.
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.