Compared to de novo drug discovery, drug repurposing
provides a time-efficient way to treat coronavirus disease 19 (COVID-19)
that is caused by severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2). SARS-CoV-2 main protease (Mpro) has been proved to be
an attractive drug target due to its pivotal involvement in viral
replication and transcription. Here, we present a graph neural network-based
deep-learning (DL) strategy to prioritize the existing drugs for their
potential therapeutic effects against SARS-CoV-2 Mpro. Mpro inhibitors
were represented as molecular graphs ready for graph attention network
(GAT) and graph isomorphism network (GIN) modeling for predicting
the inhibitory activities. The result shows that the GAT model outperforms
the GIN and other competitive models and yields satisfactory predictions
for unseen Mpro inhibitors, confirming its robustness and generalization.
The attention mechanism of GAT enables to capture the dominant substructures
and thus to realize the interpretability of the model. Finally, we
applied the optimal GAT model in conjunction with molecular docking
simulations to screen the Drug Repurposing Hub (DRH) database. As
a result, 18 drug hits with best consensus prediction scores and binding
affinity values were identified as the potential therapeutics against
COVID-19. Both the extensive literature searching and evaluations
on adsorption, distribution, metabolism, excretion, and toxicity (ADMET)
illustrate the premium drug-likeness and pharmacokinetic properties
of the drug candidates. Overall, our work not only provides an effective
GAT-based DL prediction tool for inhibitory activity of SARS-CoV-2
Mpro inhibitors but also provides theoretical guidelines for drug
discovery in the COVID-19 treatment.