Anja Kolarič (Author), Marko Jukič (Author), Urban Bren (Author)

Abstract

Viral infections pose a significant health threat worldwide. Due to the high mutation rates of many viruses and their reliance on host cellular machinery, the development of effective antiviral therapies is particularly difficult. As a result, only a limited number of antiviral agents is currently available. In parallel to modern vaccines, traditional antiviral drug development is both time-consuming and costly, underscoring the need for faster, more efficient approaches. In recent years, particularly since the beginning of the COVID-19 pandemic, machine learning (ML) together with broader artificial intelligence (AI), have emerged as powerful methodologies for drug discovery and offer the potential to accelerate the identification and development of antiviral agents. This review examines the application of ML in the early stages of antiviral drug discovery, with a particular focus on recent studies where ML methods have successfully identified hit compounds with experimentally demonstrated activity in biological assays. By highlighting these successful case studies, the review illustrates the growing impact of ML in advancing the discovery of urgently needed novel antivirals.

Keywords

strojno učenje;umetna inteligenca;biološke aktivnosti;protivirusne spojine;machine learning;artificial intelligence;antiviral compounds;biological activity;

Data

Language: English
Year of publishing:
Typology: 1.02 - Review Article
Organization: UM FKKT - Faculty of Chemistry and Chemical Engineering
Publisher: Elsevier
UDC: 577
COBISS: 253578755 Link will open in a new window
ISSN: 1464-3391
Views: 0
Downloads: 1
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Other data

Secondary language: Slovenian
Secondary keywords: strojno učenje;umetna inteligenca;biološke aktivnosti;protivirusne spojine;
Type (COBISS): Article
Pages: 36 str.
Volume: ǂVol. ǂ132
Issue: [Article no.] 118426
Chronology: 1 jan. 2026
DOI: 10.1016/j.bmc.2025.118426
ID: 27446827
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