Repositioning of 8565 Existing Drugs for COVID-19

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

doi:10.1021/acs.jpclett.0c01579

Repositioning of 8565 Existing Drugs for COVID-19

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

Research output: Contribution to journal › Review article › peer-review

80 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 language	English
Pages (from-to)	5373-5382
Number of pages	10
Journal	Journal of Physical Chemistry Letters
Volume	11
Issue number	13
DOIs	https://doi.org/10.1021/acs.jpclett.0c01579
State	Published - Jul 2 2020

Bibliographical note

Publisher Copyright:
Copyright © 2020 American Chemical Society.

Funding

This work was supported in part by NIH Grant GM126189; NSF Grants DMS-1721024, DMS-1761320, and IIS1900473; Michigan Economic Development Corporation; Bristol-Myers Squibb; and Pfizer. The authors thank The IBM TJ Watson Research Center, The COVID-19 High Performance Computing Consortium, NVIDIA, and MSU HPCC for computational assistance supporting this work.

Funders	Funder number
National Science Foundation Arctic Social Science Program	IIS1900473, DMS-1721024, DMS-1761320
National Institutes of Health (NIH)
National Institute of General Medical Sciences DP2GM119177 Sophie Dumont National Institute of General Medical Sciences	R01GM126189
Bristol-Myers Squibb
Pfizer
Michigan Economic Development Corporation

ASJC Scopus subject areas

General Materials Science
Physical and Theoretical Chemistry

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10.1021/acs.jpclett.0c01579

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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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AU - Wei, Guo Wei

PY - 2020/7/2

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Repositioning of 8565 Existing Drugs for COVID-19

Abstract

Bibliographical note

Funding

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