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?:abstract
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[Image: see text] 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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?:doi
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?:doi
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10.1021/acs.jpclett.0c01579
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document_parses/pdf_json/9d27c16192fd74632d91b2301ef90a34bda06a59.json; document_parses/pdf_json/8591bddddb70ee839f07aea29e1b5b87fddfabe3.json
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document_parses/pmc_json/PMC7313673.xml.json
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?:title
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Repositioning of 8565 Existing Drugs for COVID-19
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