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How Deep Learning in Antiviral Molecular Profiling Identified Anti-SARS-CoV-2 Inhibitors
DC Field | Value | Language |
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dc.contributor.author | 박인호 | - |
dc.contributor.author | 신전수 | - |
dc.contributor.author | 윤상선 | - |
dc.contributor.author | 김지은 | - |
dc.date.accessioned | 2024-01-03T00:18:14Z | - |
dc.date.available | 2024-01-03T00:18:14Z | - |
dc.date.issued | 2023-11 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/197222 | - |
dc.description.abstract | The integration of artificial intelligence (AI) into drug discovery has markedly advanced the search for effective therapeutics. In our study, we employed a comprehensive computational–experimental approach to identify potential anti-SARS-CoV-2 compounds. We developed a predictive model to assess the activities of compounds based on their structural features. This model screened a library of approximately 700,000 compounds, culminating in the selection of the top 100 candidates for experimental validation. In vitro assays on human intestinal epithelial cells (Caco-2) revealed that 19 of these compounds exhibited inhibitory activity. Notably, eight compounds demonstrated dose-dependent activity in Vero cell lines, with half-maximal effective concentration (EC50) values ranging from 1 μM to 7 μM. Furthermore, we utilized a clustering approach to pinpoint potential nucleoside analog inhibitors, leading to the discovery of two promising candidates: azathioprine and its metabolite, thioinosinic acid. Both compounds showed in vitro activity against SARS-CoV-2, with thioinosinic acid also significantly reducing viral loads in mouse lungs. These findings underscore the utility of AI in accelerating drug discovery processes. | - |
dc.description.statementOfResponsibility | open | - |
dc.format | application/pdf | - |
dc.language | English | - |
dc.publisher | MDPI AG | - |
dc.relation.isPartOf | BIOMEDICINES | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.title | How Deep Learning in Antiviral Molecular Profiling Identified Anti-SARS-CoV-2 Inhibitors | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | BioMedical Science Institute (의생명과학부) | - |
dc.contributor.googleauthor | Mohammed Ali | - |
dc.contributor.googleauthor | In Ho Park | - |
dc.contributor.googleauthor | Junebeom Kim | - |
dc.contributor.googleauthor | Gwanghee Kim | - |
dc.contributor.googleauthor | Jooyeon Oh | - |
dc.contributor.googleauthor | Jin Sun You | - |
dc.contributor.googleauthor | Jieun im | - |
dc.contributor.googleauthor | Jeon-Soo Shin | - |
dc.contributor.googleauthor | Sang Sun Yoon | - |
dc.identifier.doi | 10.3390/biomedicines11123134 | - |
dc.contributor.localId | A01631 | - |
dc.contributor.localId | A02144 | - |
dc.contributor.localId | A02558 | - |
dc.relation.journalcode | J03914 | - |
dc.identifier.eissn | 2227-9059 | - |
dc.subject.keyword | SARS-CoV-2 | - |
dc.subject.keyword | artificial intelligence | - |
dc.subject.keyword | compounds library | - |
dc.subject.keyword | nucleoside analogs | - |
dc.subject.keyword | azathioprine | - |
dc.subject.keyword | thioinosinic acid | - |
dc.contributor.alternativeName | Park, Inho | - |
dc.contributor.affiliatedAuthor | 박인호 | - |
dc.contributor.affiliatedAuthor | 신전수 | - |
dc.contributor.affiliatedAuthor | 윤상선 | - |
dc.citation.volume | 11 | - |
dc.citation.number | 12 | - |
dc.citation.startPage | 3134 | - |
dc.identifier.bibliographicCitation | BIOMEDICINES, Vol.11(12) : 3134, 2023-11 | - |
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