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首页> 外文期刊>Molecular informatics >QSAR Modeling of SARS-CoV M~pro Inhibitors Identifies Sufugolix, Cenicriviroc, Proglumetacin, and other Drugs as Candidates for Repurposing against SARS-CoV-2
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QSAR Modeling of SARS-CoV M~pro Inhibitors Identifies Sufugolix, Cenicriviroc, Proglumetacin, and other Drugs as Candidates for Repurposing against SARS-CoV-2

机译:SARS-COVM〜PRO抑制剂的QSAR建模鉴定了Sufugolix,Cenicriroc,Proglumetacin和其他药物作为候选者,用于评估SARS-COV-2的候选者

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摘要

The main protease (M~pro) of the SARS-CoV-2 has been proposed as one of the major drug targets for COVID-19. We have identified the experimental data on the inhibitory activity of compounds tested against the closely related (96% sequence identity, 100% active site conservation) M~pro of SARS-CoV. We developed QSAR models of these inhibitors and employed these models for virtual screening of all drugs in the DrugBank database. Similarity searching and molecular docking were explored in parallel, but docking failed to correctly discriminate between experimentally active and inactive compounds, so it was not relied upon for prospective virtual screening. Forty-two compounds were identified by our models as consensus computational hits. Subsequent to our computational studies, NCATS reported the results of experimental screening of their drug collection in SARS-CoV-2 cytopathic effect assay (https://opendata.ncats.nih.gov/covid19/). Coincidentally, NCATS tested 11 of our 42 hits, and three of them, cenicriviroc (AC_50 of 8.9 muM), proglumetacin (tested twice independently, with AC_50 of 8.9 muM and 12.5 muM), and sufugolix (AC_50 12.6 muM), were shown to be active. These observations support the value of our modeling approaches and models for guiding the experimental investigations of putative anti-COVID-19 drug candidates. All data and models used in this study are publicly available via Supplementary Materials, GitHub (https://github.com/alvesvm/sars-cov-mpro), and Chembench web portal (https://chembench.mml.unc.edu/).
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