Issue 5, 2025

Machine learning-driven antiviral libraries targeting respiratory viruses

Abstract

Viral infections represent a significant global health concern. Viral diseases can range from mild symptoms to life-threatening conditions, and the impact of these infections has grown due to increased contagious rates driven by globalization. A prime example is the SARS-CoV-2 pandemic, which emphasized the urgent need to design and develop new antiviral drugs. This study aimed to generate a curated data set of compounds relevant to respiratory infections, focusing on predicting their antiviral activity. Specifically, the study leverages ML classification models to evaluate focused and on-demand compound libraries targeting pathways associated with viral respiratory infections. ML models were trained based on the antiviral biological activity related to respiratory diseases deposited on a major public compound database annotated with biological activity. The models were validated and retrained to classify and design antiviral-focused libraries on seven respiratory targets.

Graphical abstract: Machine learning-driven antiviral libraries targeting respiratory viruses

Supplementary files

Article information

Article type
Paper
Submitted
24 Jan 2025
Accepted
02 Apr 2025
First published
04 Apr 2025
This article is Open Access
Creative Commons BY license

Digital Discovery, 2025,4, 1239-1258

Machine learning-driven antiviral libraries targeting respiratory viruses

G. Valle-Núñez, R. Cedillo-González, J. F. Avellaneda-Tamayo, F. I. Saldívar-González, D. L. Prado-Romero and J. L. Medina-Franco, Digital Discovery, 2025, 4, 1239 DOI: 10.1039/D5DD00037H

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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