Rapid Identification of Potential Inhibitors of SARS-CoV-2 Main Protease by Deep Docking of 1.3 Billion Compounds.
Identifieur interne : 000621 ( PubMed/Curation ); précédent : 000620; suivant : 000622Rapid Identification of Potential Inhibitors of SARS-CoV-2 Main Protease by Deep Docking of 1.3 Billion Compounds.
Auteurs : Anh-Tien Ton [Canada] ; Francesco Gentile [Canada] ; Michael Hsing [Canada] ; Fuqiang Ban [Canada] ; Artem Cherkasov [Canada]Source :
- Molecular informatics [ 1868-1751 ] ; 2020.
Abstract
The recently emerged 2019 Novel Coronavirus (SARS-CoV-2) and associated COVID-19 disease cause serious or even fatal respiratory tract infection and yet no approved therapeutics or effective treatment is currently available to effectively combat the outbreak. This urgent situation is pressing the world to respond with the development of novel vaccine or a small molecule therapeutics for SARS-CoV-2. Along these efforts, the structure of SARS-CoV-2 main protease (Mpro) has been rapidly resolved and made publicly available to facilitate global efforts to develop novel drug candidates. Recently, our group has developed a novel deep learning platform - Deep Docking (DD) which provides fast prediction of docking scores of Glide (or any other docking program) and, hence, enables structure-based virtual screening of billions of purchasable molecules in a short time. In the current study we applied DD to all 1.3 billion compounds from ZINC15 library to identify top 1,000 potential ligands for SARS-CoV-2 Mpro protein. The compounds are made publicly available for further characterization and development by scientific community.
DOI: 10.1002/minf.202000028
PubMed: 32162456
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sortKey="Gentile, Francesco" sort="Gentile, Francesco" uniqKey="Gentile F" first="Francesco" last="Gentile">Francesco Gentile</name><affiliation wicri:level="1"><nlm:affiliation>Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, BC, V6H 3Z6, Canada.</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, BC, V6H 3Z6</wicri:regionArea></affiliation></author><author><name sortKey="Hsing, Michael" sort="Hsing, Michael" uniqKey="Hsing M" first="Michael" last="Hsing">Michael Hsing</name><affiliation wicri:level="1"><nlm:affiliation>Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, BC, V6H 3Z6, Canada.</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, BC, V6H 3Z6</wicri:regionArea></affiliation></author><author><name sortKey="Ban, Fuqiang" sort="Ban, Fuqiang" uniqKey="Ban F" first="Fuqiang" last="Ban">Fuqiang Ban</name><affiliation wicri:level="1"><nlm:affiliation>Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, BC, V6H 3Z6, Canada.</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, BC, V6H 3Z6</wicri:regionArea></affiliation></author><author><name sortKey="Cherkasov, Artem" sort="Cherkasov, Artem" uniqKey="Cherkasov A" first="Artem" last="Cherkasov">Artem Cherkasov</name><affiliation wicri:level="1"><nlm:affiliation>Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, BC, V6H 3Z6, Canada.</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, BC, V6H 3Z6</wicri:regionArea></affiliation></author></analytic><series><title level="j">Molecular informatics</title><idno type="eISSN">1868-1751</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The recently emerged 2019 Novel Coronavirus (SARS-CoV-2) and associated COVID-19 disease cause serious or even fatal respiratory tract infection and yet no approved therapeutics or effective treatment is currently available to effectively combat the outbreak. This urgent situation is pressing the world to respond with the development of novel vaccine or a small molecule therapeutics for SARS-CoV-2. Along these efforts, the structure of SARS-CoV-2 main protease (Mpro) has been rapidly resolved and made publicly available to facilitate global efforts to develop novel drug candidates. Recently, our group has developed a novel deep learning platform - Deep Docking (DD) which provides fast prediction of docking scores of Glide (or any other docking program) and, hence, enables structure-based virtual screening of billions of purchasable molecules in a short time. In the current study we applied DD to all 1.3 billion compounds from ZINC15 library to identify top 1,000 potential ligands for SARS-CoV-2 Mpro protein. The compounds are made publicly available for further characterization and development by scientific community.</div></front></TEI><pubmed><MedlineCitation Status="Publisher" Owner="NLM"><PMID Version="1">32162456</PMID><DateRevised><Year>2020</Year><Month>03</Month><Day>26</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1868-1751</ISSN><JournalIssue CitedMedium="Internet"><PubDate><Year>2020</Year><Month>Mar</Month><Day>11</Day></PubDate></JournalIssue><Title>Molecular informatics</Title><ISOAbbreviation>Mol Inform</ISOAbbreviation></Journal><ArticleTitle>Rapid Identification of Potential Inhibitors of SARS-CoV-2 Main Protease by Deep Docking of 1.3 Billion Compounds.</ArticleTitle><ELocationID EIdType="doi" ValidYN="Y">10.1002/minf.202000028</ELocationID><Abstract><AbstractText>The recently emerged 2019 Novel Coronavirus (SARS-CoV-2) and associated COVID-19 disease cause serious or even fatal respiratory tract infection and yet no approved therapeutics or effective treatment is currently available to effectively combat the outbreak. This urgent situation is pressing the world to respond with the development of novel vaccine or a small molecule therapeutics for SARS-CoV-2. Along these efforts, the structure of SARS-CoV-2 main protease (Mpro) has been rapidly resolved and made publicly available to facilitate global efforts to develop novel drug candidates. Recently, our group has developed a novel deep learning platform - Deep Docking (DD) which provides fast prediction of docking scores of Glide (or any other docking program) and, hence, enables structure-based virtual screening of billions of purchasable molecules in a short time. In the current study we applied DD to all 1.3 billion compounds from ZINC15 library to identify top 1,000 potential ligands for SARS-CoV-2 Mpro protein. The compounds are made publicly available for further characterization and development by scientific community.</AbstractText><CopyrightInformation>© 2020 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Ton</LastName><ForeName>Anh-Tien</ForeName><Initials>AT</Initials><AffiliationInfo><Affiliation>Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, BC, V6H 3Z6, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Gentile</LastName><ForeName>Francesco</ForeName><Initials>F</Initials><Identifier Source="ORCID">http://orcid.org/0000-0001-8299-1976</Identifier><AffiliationInfo><Affiliation>Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, BC, V6H 3Z6, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Hsing</LastName><ForeName>Michael</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, BC, V6H 3Z6, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ban</LastName><ForeName>Fuqiang</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, BC, V6H 3Z6, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Cherkasov</LastName><ForeName>Artem</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Vancouver Prostate Centre, University of British Columbia, 2660 Oak Street, Vancouver, BC, V6H 3Z6, Canada.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>DC0190GP</GrantID><Agency>CIHR Canadian 2019 Novel Coronavirus (2019-nCoV)</Agency><Country></Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>03</Month><Day>11</Day></ArticleDate></Article><MedlineJournalInfo><Country>Germany</Country><MedlineTA>Mol Inform</MedlineTA><NlmUniqueID>101529315</NlmUniqueID><ISSNLinking>1868-1743</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">COVID-19</Keyword><Keyword MajorTopicYN="N">SARS-CoV-2</Keyword><Keyword MajorTopicYN="N">deep learning</Keyword><Keyword MajorTopicYN="N">protease inhibitors</Keyword><Keyword MajorTopicYN="N">virtual screening</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>02</Month><Day>22</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>03</Month><Day>11</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>3</Month><Day>13</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>3</Month><Day>13</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>3</Month><Day>13</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>aheadofprint</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32162456</ArticleId><ArticleId IdType="doi">10.1002/minf.202000028</ArticleId></ArticleIdList><ReferenceList><Title>References</Title><Reference><Citation>A. 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