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10.1021/acs.jpclett.0c01579

http://scihub22266oqcxt.onion/10.1021/acs.jpclett.0c01579
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32543196!7313673!32543196
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suck abstract from ncbi

pmid32543196      J+Phys+Chem+Lett 2020 ; 11 (13): 5373-5382
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  • Repositioning of 8565 Existing Drugs for COVID-19 #MMPMID32543196
  • Gao K; Nguyen DD; Chen J; Wang R; Wei GW
  • J Phys Chem Lett 2020[Jul]; 11 (13): 5373-5382 PMID32543196show ga
  • 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.
  • |*Drug Repositioning[MESH]
  • |Antiviral Agents/*metabolism[MESH]
  • |Betacoronavirus/enzymology[MESH]
  • |COVID-19[MESH]
  • |COVID-19 Drug Treatment[MESH]
  • |Coronavirus 3C Proteases[MESH]
  • |Coronavirus Infections/*drug therapy/enzymology[MESH]
  • |Cysteine Endopeptidases/metabolism[MESH]
  • |Cysteine Proteinase Inhibitors/*metabolism[MESH]
  • |Databases, Protein/statistics & numerical data[MESH]
  • |Humans[MESH]
  • |Machine Learning[MESH]
  • |Pandemics[MESH]
  • |Pneumonia, Viral/*drug therapy/enzymology[MESH]
  • |Protein Binding[MESH]
  • |SARS-CoV-2[MESH]


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