Transferable Graph Neural Fingerprint Models for Quick Response to Future Bio-Threats

TL;DR

This paper develops transferable graph neural fingerprint models for rapid and accurate virtual screening of drug molecules, demonstrating high accuracy and efficiency on COVID-19 targets and potential for future bio-threats.

Contribution

It introduces a transferable graph neural fingerprint method trained on multiple targets, improving efficiency and generalization over traditional target-specific models.

Findings

Achieved mean squared error below 0.21 kcal/mol on docking scores.

Transferable models perform comparably to target-specific models.

Method is adaptable for rapid screening of future bio-threats.

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.