Uika Koshimizu, Junichi Ono, Y. Fukunishi, Hiromi Nakai
{"title":"Hybrid in Silico Drug Discovery Study toward the Development of Oral Antivirals for COVID-19","authors":"Uika Koshimizu, Junichi Ono, Y. Fukunishi, Hiromi Nakai","doi":"10.2477/jccj.2022-0029","DOIUrl":null,"url":null,"abstract":"Hybrid in silico drug discovery was performed by combining large-scale quantum molecular dynamics (QMD) simulations with the conventional in silico drug discovery, focusing on developing covalent inhibitors against the main protease (M-pro) of SARS-CoV-2, the virus responsible for ongoing COVID-19 pandemic. The crystal structures and instantaneous structures obtained from the large-scale QMD simulations for M-pro were used as receptors in ensemble docking to estimate the binding affinities of the four ligands: the natural substrate recognized by M-pro, that recognized by the other enzyme of SARS-CoV-2, approved covalent inhibitor (PF-07321332), and the new candidate compound X determined from virtual screening. The present result shows that the binding affinity of X was comparable to that of PF-07321332, demonstrating the potency of our drug discovery.","PeriodicalId":41909,"journal":{"name":"Journal of Computer Chemistry-Japan","volume":"1 1","pages":""},"PeriodicalIF":0.1000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computer Chemistry-Japan","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2477/jccj.2022-0029","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Hybrid in silico drug discovery was performed by combining large-scale quantum molecular dynamics (QMD) simulations with the conventional in silico drug discovery, focusing on developing covalent inhibitors against the main protease (M-pro) of SARS-CoV-2, the virus responsible for ongoing COVID-19 pandemic. The crystal structures and instantaneous structures obtained from the large-scale QMD simulations for M-pro were used as receptors in ensemble docking to estimate the binding affinities of the four ligands: the natural substrate recognized by M-pro, that recognized by the other enzyme of SARS-CoV-2, approved covalent inhibitor (PF-07321332), and the new candidate compound X determined from virtual screening. The present result shows that the binding affinity of X was comparable to that of PF-07321332, demonstrating the potency of our drug discovery.