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How Deep Learning in Antiviral Molecular Profiling Identified Anti-SARS-CoV-2 Inhibitors

Authors
 Mohammed Ali  ;  In Ho Park  ;  Junebeom Kim  ;  Gwanghee Kim  ;  Jooyeon Oh  ;  Jin Sun You  ;  Jieun im  ;  Jeon-Soo Shin  ;  Sang Sun Yoon 
Citation
 BIOMEDICINES, Vol.11(12) : 3134, 2023-11 
Journal Title
BIOMEDICINES
Issue Date
2023-11
Keywords
SARS-CoV-2 ; artificial intelligence ; compounds library ; nucleoside analogs ; azathioprine ; thioinosinic acid
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.
Files in This Item:
T202307012.pdf Download
DOI
10.3390/biomedicines11123134
Appears in Collections:
1. College of Medicine (의과대학) > BioMedical Science Institute (의생명과학부) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Microbiology (미생물학교실) > 1. Journal Papers
Yonsei Authors
Kim, Ji-Eun(김지은)
Park, Inho(박인호) ORCID logo https://orcid.org/0000-0003-2190-5469
Shin, Jeon Soo(신전수) ORCID logo https://orcid.org/0000-0002-8294-3234
Yoon, Sang Sun(윤상선) ORCID logo https://orcid.org/0000-0003-2979-365X
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/197222
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