Repositioning of 8565 Existing Drugs for COVID-19

Kaifu Gao, Duc Duy Nguyen, Jiahui Chen, Rui Wang, Guo Wei Wei

Research output: Contribution to journalReview articlepeer-review

69 Scopus citations

Abstract

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected over 7.1 million people and led to over 0.4 million deaths. Currently, there is no specific anti-SARS-CoV-2 medication. New drug discovery typically takes more than 10 years. Drug repositioning becomes one of the most feasible approaches for combating COVID-19. This work curates the largest available experimental data set for SARS-CoV-2 or SARS-CoV 3CL (main) protease inhibitors. On the basis of this data set, we develop validated machine learning models with relatively low root-mean-square error to screen 1553 FDA-approved drugs as well as another 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 3CL protease inhibitors is analyzed. This work offers a foundation for further experimental studies of COVID-19 drug repositioning.

Original languageEnglish
Pages (from-to)5373-5382
Number of pages10
JournalJournal of Physical Chemistry Letters
Volume11
Issue number13
DOIs
StatePublished - Jul 2 2020

Bibliographical note

Publisher Copyright:
Copyright © 2020 American Chemical Society.

ASJC Scopus subject areas

  • General Materials Science
  • Physical and Theoretical Chemistry

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