Asset Details
Do you wish to reserve the book?
Leveraging viral genome sequences and machine learning models for identification of potentially selective antiviral agents
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Leveraging viral genome sequences and machine learning models for identification of potentially selective antiviral agents
Do you wish to request the book?
Leveraging viral genome sequences and machine learning models for identification of potentially selective antiviral agents
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Leveraging viral genome sequences and machine learning models for identification of potentially selective antiviral agents
2025
Overview
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.
Leveraging viral genome sequencing for antiviral drug development remains underexplored in machine learning applications. Here, the authors integrate viral genome sequences with drug structural data to create robust predictive models, identifying potential antivirals (e.g., anti-SARS-CoV-2 compounds).
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
