Repositioning of 8565 existing drugs for COVID-19

TL;DR

This study leverages machine learning models trained on experimental data to identify existing FDA-approved and investigational drugs that could potentially inhibit SARS-CoV-2, aiding rapid COVID-19 drug repurposing efforts.

Contribution

It presents the largest curated dataset of SARS-CoV-2 protease inhibitors and develops validated machine learning models for screening existing drugs for COVID-19 treatment.

Findings

Many existing drugs may be potent against SARS-CoV-2

Developed models show low prediction error for drug screening

Provides a foundation for experimental validation of drug candidates

Abstract

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected near 5 million people and led to over 0.3 million deaths. Currently, there is no specific anti-SARS-CoV-2 medication. New drug discovery typically takes more than ten years. Drug repositioning becomes one of the most feasible approaches for combating COVID-19. This work curates the largest available experimental dataset for SARS-CoV-2 or SARS-CoV main protease inhibitors. Based on this dataset, we develop validated machine learning models with relatively low root mean square error to screen 1553 FDA-approved drugs as well as other 7012 investigational or off-market drugs in DrugBank. We found that many existing drugs might be potentially potent to SARS-CoV-2. The druggability of many potent SARS-CoV-2 main protease inhibitors is analyzed. This work offers a foundation for further experimental studies of COVID-19 drug repositioning.