Julien Hazemann, Thierry Kimmerlin, Roland Lange, Aengus Mac Sweeney, Geoffroy Bourquin, Daniel Ritz and Paul Czodrowski
{"title":"Identification of SARS-CoV-2 Mpro inhibitors through deep reinforcement learning for de novo drug design and computational chemistry approaches†","authors":"Julien Hazemann, Thierry Kimmerlin, Roland Lange, Aengus Mac Sweeney, Geoffroy Bourquin, Daniel Ritz and Paul Czodrowski","doi":"10.1039/D4MD00106K","DOIUrl":null,"url":null,"abstract":"<p >Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic of coronavirus disease (COVID-19) since its emergence in December 2019. As of January 2024, there has been over 774 million reported cases and 7 million deaths worldwide. While vaccination efforts have been successful in reducing the severity of the disease and decreasing the transmission rate, the development of effective therapeutics against SARS-CoV-2 remains a critical need. The main protease (Mpro) of SARS-CoV-2 is an essential enzyme required for viral replication and has been identified as a promising target for drug development. In this study, we report the identification of novel Mpro inhibitors, using a combination of deep reinforcement learning for <em>de novo</em> drug design with 3D pharmacophore/shape-based alignment and privileged fragment match count scoring components followed by hit expansions and molecular docking approaches. Our experimentally validated results show that 3 novel series exhibit potent inhibitory activity against SARS-CoV-2 Mpro, with IC<small><sub>50</sub></small> values ranging from 1.3 μM to 2.3 μM and a high degree of selectivity. These findings represent promising starting points for the development of new antiviral therapies against COVID-19.</p>","PeriodicalId":21462,"journal":{"name":"RSC medicinal chemistry","volume":null,"pages":null},"PeriodicalIF":4.1000,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2024/md/d4md00106k?page=search","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"RSC medicinal chemistry","FirstCategoryId":"3","ListUrlMain":"https://pubs.rsc.org/en/content/articlelanding/2024/md/d4md00106k","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic of coronavirus disease (COVID-19) since its emergence in December 2019. As of January 2024, there has been over 774 million reported cases and 7 million deaths worldwide. While vaccination efforts have been successful in reducing the severity of the disease and decreasing the transmission rate, the development of effective therapeutics against SARS-CoV-2 remains a critical need. The main protease (Mpro) of SARS-CoV-2 is an essential enzyme required for viral replication and has been identified as a promising target for drug development. In this study, we report the identification of novel Mpro inhibitors, using a combination of deep reinforcement learning for de novo drug design with 3D pharmacophore/shape-based alignment and privileged fragment match count scoring components followed by hit expansions and molecular docking approaches. Our experimentally validated results show that 3 novel series exhibit potent inhibitory activity against SARS-CoV-2 Mpro, with IC50 values ranging from 1.3 μM to 2.3 μM and a high degree of selectivity. These findings represent promising starting points for the development of new antiviral therapies against COVID-19.