  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.
