Journal
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
Volume 11, Issue 13, Pages 5373-5382Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jpclett.0c01579
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Funding
- NIH [GM126189]
- NSF [DMS-1721024, DMS-1761320, IIS1900473]
- Michigan Economic Development Corporation
- Bristol-Myers Squibb
- Pfizer
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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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