Journal Article Epublish
PubMed: 40542184 DOI: 10.1038/s42004-025-01583-2
Leveraging viral genome sequences and machine learning models for identification of potentially selective antiviral agents.
Commun Chem 8(1):189
Authors: Tuan Xu, Miao Xu, Qi Zhang, Catherine Z Chen, Wei Zheng, Ruili Huang
Affiliations
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, USA.
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, USA.
Abstract
Viral genome sequencing provides valuable information for antiviral development, yet its integration with machine learning for virtual screening remains underexplored. To bridge this gap, viral genome sequences were combined with structural data of approved and investigational antivirals to identify virus-selective agents. In parallel, quantitative structure-activity relationship (QSAR) models were built to predict pan-antivirals. Robust models were generated with the area under the receiver operating characteristic curve (AUC-ROC) >0.72 for virus-selective and >0.79 for pan-antiviral predictions. These models were applied to virtually screen ~360 K compounds for anti-SARS-CoV-2 activity. The 346 compounds identified by the models were tested using two in vitro assays, yielding hit rates of 9.4% (24/256) in the pseudotyped particle (PP) entry assay and 37% (47/128) in the RNA-dependent RNA polymerase (RdRp) assay. The top compounds showed potencies around 1 µM. This study provides a framework for virtual screening of virus-selective and pan- antivirals against emerging pathogens.
MeSH
Genome, Machine Learning, Quantitative Structure-Activity Relationship, Viruses, Genome, Viral, Learning, Antiviral Agents