A Transferable Deep Learning Approach to Fast Screen Potent Antiviral Drugs against SARS-CoV-2

Abstract

The COVID-19 pandemic calls for rapid development of effective treatments. Although various drug repurpose approaches have been used to screen the FDA-approved drugs and drug candidates in clinical phases against SARS-CoV-2, the coronavirus that causes this disease, no magic bullets have been found until now. We used directed message passing neural network to first build a broad-spectrum anti-beta-coronavirus compound prediction model, which gave satisfactory predictions on newly reported active compounds against SARS-CoV-2. Then we applied transfer learning to fine-tune the model with the recently reported anti-SARS-CoV-2 compounds. The fine-tuned model was applied to screen a large compound library with 4.9 million drug-like molecules from ZINC15 database and recommended a list of potential anti-SARS-CoV-2 compounds for further experimental testing. As a proof-of-concept, we experimentally tested 7 high-scored compounds that also demonstrated good binding strength in docking study against the 3C-like protease of SARS-CoV-2 and found one novel compound that inhibited the enzyme with an IC50 of 37.0 μM. Our model is highly efficient and can be used to screen large compound databases with billions or more compounds to accelerate the drug discovery process for the treatment of COVID-19.

Spread and Dynamics of COVID-19 in the Kingdom of Saudi Arabia and Four Other Countries in the Early Phase: A Study for the Development of the Advanced Health System

“…eVir uses embeddings of drugs to capture their drug-protein interactions, which are compared against embeddings of known antiviral peptides. Recently transferable deep learning approach utilized for fast screening of antiviral compound for the management and treatment of COVID-19 (Wang et al, 2021).…”