Quantitative Structure-Activity Relationship Machine Learning Models and their Applications for Identifying Viral 3CLpro- and RdRp-Targeting Compounds as Potential Therapeutics for COVID-19 and Related Viral Infections.
Journal
ACS omega
ISSN: 2470-1343
Titre abrégé: ACS Omega
Pays: United States
ID NLM: 101691658
Informations de publication
Date de publication:
27 Oct 2020
27 Oct 2020
Historique:
received:
31
07
2020
accepted:
29
09
2020
entrez:
2
11
2020
pubmed:
3
11
2020
medline:
3
11
2020
Statut:
epublish
Résumé
In response to the ongoing COVID-19 pandemic, there is a worldwide effort being made to identify potential anti-SARS-CoV-2 therapeutics. Here, we contribute to these efforts by building machine-learning predictive models to identify novel drug candidates for the viral targets 3 chymotrypsin-like protease (3CLpro) and RNA-dependent RNA polymerase (RdRp). Chemist-curated training sets of substances were assembled from CAS data collections and integrated with curated bioassay data. The best-performing classification models were applied to screen a set of FDA-approved drugs and CAS REGISTRY substances that are similar to, or associated with, antiviral agents. Numerous substances with potential activity against 3CLpro or RdRp were found, and some were validated by published bioassay studies and/or by their inclusion in upcoming or ongoing COVID-19 clinical trials. This study further supports that machine learning-based predictive models may be used to assist the drug discovery process for COVID-19 and other diseases.
Identifiants
pubmed: 33134697
doi: 10.1021/acsomega.0c03682
pmc: PMC7571315
doi:
Types de publication
Journal Article
Langues
eng
Pagination
27344-27358Informations de copyright
© 2020 American Chemical Society.
Déclaration de conflit d'intérêts
The authors declare no competing financial interest.
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