Repositioning of 8565 Existing Drugs for COVID-19

  • J Phys Chem Lett. 2020 Jul 2;11(13):5373-5382. doi: 10.1021/acs.jpclett.0c01579.
Kaifu Gao  1 ,  Duc Duy Nguyen  2 ,  Jiahui Chen  1 ,  Rui Wang  1 ,  Guo-Wei Wei  1  3  4
Affiliations
  • 1. Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States.
  • 2. Department of Mathematics, University of Kentucky, Lexington, Kentucky 40506, United States.
  • 3. Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States.
  • 4. Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824, United States.
PMID: 32543196 DOI: 10.1021/acs.jpclett.0c01579
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.

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