Computational prediction of interactions between Paxlovid and prescription drugs

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dc.contributor.authorKim, Yejiko
dc.contributor.authorRyu, Jae Yongko
dc.contributor.authorKim, Hyun Ukko
dc.contributor.authorLee, Sang Yupko
dc.date.accessioned2023-05-12T02:01:41Z-
dc.date.available2023-05-12T02:01:41Z-
dc.date.created2023-05-08-
dc.date.created2023-05-08-
dc.date.issued2023-03-
dc.identifier.citationPROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, v.120, no.12-
dc.identifier.issn0027-8424-
dc.identifier.urihttp://hdl.handle.net/10203/306750-
dc.description.abstractPfizer’s Paxlovid has recently been approved for the emergency use authorization (EUA) from the US Food and Drug Administration (FDA) for the treatment of mild-to-moderate COVID-19. Drug interactions can be a serious medical problem for COVID-19 patients with underlying medical conditions, such as hypertension and diabetes, who have likely been taking other drugs. Here, we use deep learning to predict potential drug–drug interactions between Paxlovid components (nirmatrelvir and ritonavir) and 2,248 prescription drugs for treating various diseases.-
dc.languageEnglish-
dc.publisherNATL ACAD SCIENCES-
dc.titleComputational prediction of interactions between Paxlovid and prescription drugs-
dc.typeArticle-
dc.identifier.wosid000991153200014-
dc.identifier.scopusid2-s2.0-85150143933-
dc.type.rimsART-
dc.citation.volume120-
dc.citation.issue12-
dc.citation.publicationnamePROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA-
dc.identifier.doi10.1073/pnas.2221857120-
dc.contributor.localauthorKim, Hyun Uk-
dc.contributor.localauthorLee, Sang Yup-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorCOVID-19-
dc.subject.keywordAuthorDeepDDI2-
dc.subject.keywordAuthordrug interactions-
dc.subject.keywordAuthorPaxlovid-
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CBE-Journal Papers(저널논문)
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