A transferable deep learning approach to fast screen potential antiviral drugs against SARS-CoV-2

• Published in: Briefings in Bioinformatics Vol. 22; no. 6
• Main Authors: Wang, Shiwei, Sun, Qi, Xu, Youjun, Pei, Jianfeng, Lai, Luhua
• Format: Journal Article Web Resource
• Language: English
• Published: Oxford Oxford University Press 05.11.2021
• Subjects: Problem Solving Protocol
• ISSN: 1467-5463; 1477-4054
• DOI: 10.1093/bib/bbab211

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. In this study, 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 and derived a SARS-CoV-2 specific prediction model COVIDVS-3. We used COVIDVS-3 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 seven high-scored compounds that also demonstrated good binding strength in docking studies against the 3C-like protease of SARS-CoV-2 and found one novel compound that can inhibit the enzyme. Our model is highly efficient and can be used to screen large compound databases with millions or more compounds to accelerate the drug discovery process for the treatment of COVID-19.