Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model.
Identifieur interne : 000213 ( PubMed/Corpus ); précédent : 000212; suivant : 000214Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model.
Auteurs : Bo Ram Beck ; Bonggun Shin ; Yoonjung Choi ; Sungsoo Park ; Keunsoo KangSource :
- Computational and structural biotechnology journal [ 2001-0370 ] ; 2020.
Abstract
The infection of a novel coronavirus found in Wuhan of China (SARS-CoV-2) is rapidly spreading, and the incidence rate is increasing worldwide. Due to the lack of effective treatment options for SARS-CoV-2, various strategies are being tested in China, including drug repurposing. In this study, we used our pre-trained deep learning-based drug-target interaction model called Molecule Transformer-Drug Target Interaction (MT-DTI) to identify commercially available drugs that could act on viral proteins of SARS-CoV-2. The result showed that atazanavir, an antiretroviral medication used to treat and prevent the human immunodeficiency virus (HIV), is the best chemical compound, showing an inhibitory potency with Kd of 94.94 nM against the SARS-CoV-2 3C-like proteinase, followed by remdesivir (113.13 nM), efavirenz (199.17 nM), ritonavir (204.05 nM), and dolutegravir (336.91 nM). Interestingly, lopinavir, ritonavir, and darunavir are all designed to target viral proteinases. However, in our prediction, they may also bind to the replication complex components of SARS-CoV-2 with an inhibitory potency with Kd < 1000 nM. In addition, we also found that several antiviral agents, such as Kaletra (lopinavir/ritonavir), could be used for the treatment of SARS-CoV-2. Overall, we suggest that the list of antiviral drugs identified by the MT-DTI model should be considered, when establishing effective treatment strategies for SARS-CoV-2.
DOI: 10.1016/j.csbj.2020.03.025
PubMed: 32280433
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model.</title><author><name sortKey="Beck, Bo Ram" sort="Beck, Bo Ram" uniqKey="Beck B" first="Bo Ram" last="Beck">Bo Ram Beck</name><affiliation><nlm:affiliation>Deargen, Inc., Daejeon, Republic of Korea.</nlm:affiliation></affiliation></author><author><name sortKey="Shin, Bonggun" sort="Shin, Bonggun" uniqKey="Shin B" first="Bonggun" last="Shin">Bonggun Shin</name><affiliation><nlm:affiliation>Deargen, Inc., Daejeon, Republic of Korea.</nlm:affiliation></affiliation></author><author><name sortKey="Choi, Yoonjung" sort="Choi, Yoonjung" uniqKey="Choi Y" first="Yoonjung" last="Choi">Yoonjung Choi</name><affiliation><nlm:affiliation>Deargen, Inc., Daejeon, Republic of Korea.</nlm:affiliation></affiliation></author><author><name sortKey="Park, Sungsoo" sort="Park, Sungsoo" uniqKey="Park S" first="Sungsoo" last="Park">Sungsoo Park</name><affiliation><nlm:affiliation>Deargen, Inc., Daejeon, Republic of Korea.</nlm:affiliation></affiliation></author><author><name sortKey="Kang, Keunsoo" sort="Kang, Keunsoo" uniqKey="Kang K" first="Keunsoo" last="Kang">Keunsoo Kang</name><affiliation><nlm:affiliation>Department of Microbiology, College of Natural Sciences, Dankook University, Cheonan, Republic of Korea.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:32280433</idno><idno type="pmid">32280433</idno><idno type="doi">10.1016/j.csbj.2020.03.025</idno><idno type="wicri:Area/PubMed/Corpus">000213</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000213</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model.</title><author><name sortKey="Beck, Bo Ram" sort="Beck, Bo Ram" uniqKey="Beck B" first="Bo Ram" last="Beck">Bo Ram Beck</name><affiliation><nlm:affiliation>Deargen, Inc., Daejeon, Republic of Korea.</nlm:affiliation></affiliation></author><author><name sortKey="Shin, Bonggun" sort="Shin, Bonggun" uniqKey="Shin B" first="Bonggun" last="Shin">Bonggun Shin</name><affiliation><nlm:affiliation>Deargen, Inc., Daejeon, Republic of Korea.</nlm:affiliation></affiliation></author><author><name sortKey="Choi, Yoonjung" sort="Choi, Yoonjung" uniqKey="Choi Y" first="Yoonjung" last="Choi">Yoonjung Choi</name><affiliation><nlm:affiliation>Deargen, Inc., Daejeon, Republic of Korea.</nlm:affiliation></affiliation></author><author><name sortKey="Park, Sungsoo" sort="Park, Sungsoo" uniqKey="Park S" first="Sungsoo" last="Park">Sungsoo Park</name><affiliation><nlm:affiliation>Deargen, Inc., Daejeon, Republic of Korea.</nlm:affiliation></affiliation></author><author><name sortKey="Kang, Keunsoo" sort="Kang, Keunsoo" uniqKey="Kang K" first="Keunsoo" last="Kang">Keunsoo Kang</name><affiliation><nlm:affiliation>Department of Microbiology, College of Natural Sciences, Dankook University, Cheonan, Republic of Korea.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Computational and structural biotechnology journal</title><idno type="ISSN">2001-0370</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 infection of a novel coronavirus found in Wuhan of China (SARS-CoV-2) is rapidly spreading, and the incidence rate is increasing worldwide. Due to the lack of effective treatment options for SARS-CoV-2, various strategies are being tested in China, including drug repurposing. In this study, we used our pre-trained deep learning-based drug-target interaction model called Molecule Transformer-Drug Target Interaction (MT-DTI) to identify commercially available drugs that could act on viral proteins of SARS-CoV-2. The result showed that atazanavir, an antiretroviral medication used to treat and prevent the human immunodeficiency virus (HIV), is the best chemical compound, showing an inhibitory potency with <i>K<sub>d</sub></i> of 94.94 nM against the SARS-CoV-2 3C-like proteinase, followed by remdesivir (113.13 nM), efavirenz (199.17 nM), ritonavir (204.05 nM), and dolutegravir (336.91 nM). Interestingly, lopinavir, ritonavir, and darunavir are all designed to target viral proteinases. However, in our prediction, they may also bind to the replication complex components of SARS-CoV-2 with an inhibitory potency with <i>K<sub>d</sub></i> < 1000 nM. In addition, we also found that several antiviral agents, such as Kaletra (lopinavir/ritonavir), could be used for the treatment of SARS-CoV-2. Overall, we suggest that the list of antiviral drugs identified by the MT-DTI model should be considered, when establishing effective treatment strategies for SARS-CoV-2.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">32280433</PMID><DateRevised><Year>2020</Year><Month>04</Month><Day>20</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Print">2001-0370</ISSN><JournalIssue CitedMedium="Print"><Volume>18</Volume><PubDate><Year>2020</Year></PubDate></JournalIssue><Title>Computational and structural biotechnology journal</Title><ISOAbbreviation>Comput Struct Biotechnol J</ISOAbbreviation></Journal><ArticleTitle>Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model.</ArticleTitle><Pagination><MedlinePgn>784-790</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.csbj.2020.03.025</ELocationID><Abstract><AbstractText>The infection of a novel coronavirus found in Wuhan of China (SARS-CoV-2) is rapidly spreading, and the incidence rate is increasing worldwide. Due to the lack of effective treatment options for SARS-CoV-2, various strategies are being tested in China, including drug repurposing. In this study, we used our pre-trained deep learning-based drug-target interaction model called Molecule Transformer-Drug Target Interaction (MT-DTI) to identify commercially available drugs that could act on viral proteins of SARS-CoV-2. The result showed that atazanavir, an antiretroviral medication used to treat and prevent the human immunodeficiency virus (HIV), is the best chemical compound, showing an inhibitory potency with <i>K<sub>d</sub></i> of 94.94 nM against the SARS-CoV-2 3C-like proteinase, followed by remdesivir (113.13 nM), efavirenz (199.17 nM), ritonavir (204.05 nM), and dolutegravir (336.91 nM). Interestingly, lopinavir, ritonavir, and darunavir are all designed to target viral proteinases. However, in our prediction, they may also bind to the replication complex components of SARS-CoV-2 with an inhibitory potency with <i>K<sub>d</sub></i> < 1000 nM. In addition, we also found that several antiviral agents, such as Kaletra (lopinavir/ritonavir), could be used for the treatment of SARS-CoV-2. Overall, we suggest that the list of antiviral drugs identified by the MT-DTI model should be considered, when establishing effective treatment strategies for SARS-CoV-2.</AbstractText><CopyrightInformation>© 2020 The Authors.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Beck</LastName><ForeName>Bo Ram</ForeName><Initials>BR</Initials><AffiliationInfo><Affiliation>Deargen, Inc., Daejeon, Republic of Korea.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Shin</LastName><ForeName>Bonggun</ForeName><Initials>B</Initials><AffiliationInfo><Affiliation>Deargen, Inc., Daejeon, Republic of Korea.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Computer Science, Emory University, Atlanta, GA, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Choi</LastName><ForeName>Yoonjung</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>Deargen, Inc., Daejeon, Republic of Korea.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Park</LastName><ForeName>Sungsoo</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Deargen, Inc., Daejeon, Republic of Korea.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kang</LastName><ForeName>Keunsoo</ForeName><Initials>K</Initials><AffiliationInfo><Affiliation>Department of Microbiology, College of Natural Sciences, Dankook University, Cheonan, Republic of Korea.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>03</Month><Day>30</Day></ArticleDate></Article><MedlineJournalInfo><Country>Netherlands</Country><MedlineTA>Comput Struct Biotechnol J</MedlineTA><NlmUniqueID>101585369</NlmUniqueID><ISSNLinking>2001-0370</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Atazanavir</Keyword><Keyword MajorTopicYN="N">COVID-19</Keyword><Keyword MajorTopicYN="N">Coronavirus</Keyword><Keyword MajorTopicYN="N">Deep learning</Keyword><Keyword MajorTopicYN="N">Drug repurposing</Keyword><Keyword MajorTopicYN="N">MT-DTI</Keyword><Keyword MajorTopicYN="N">SARS-CoV-2</Keyword></KeywordList><CoiStatement>Beck B.R., Choi Y., and Park S. are employed by company Deargen Inc. Shin B. is employed by Deargen Inc as a part-time advisor. Kang K. is one of the co-founders of, and a shareholder in, Deargen Inc.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>02</Month><Day>22</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2020</Year><Month>03</Month><Day>23</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>03</Month><Day>25</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>4</Month><Day>14</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>4</Month><Day>14</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>4</Month><Day>14</Day><Hour>6</Hour><Minute>1</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32280433</ArticleId><ArticleId IdType="doi">10.1016/j.csbj.2020.03.025</ArticleId><ArticleId IdType="pii">S2001-0370(20)30049-0</ArticleId><ArticleId IdType="pmc">PMC7118541</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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