| Title: | Machine learning in antiviral drug design |
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| Authors: | ID Kolarič, Anja (Author)
ID Jukič, Marko (Author)
ID Bren, Urban (Author) |
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| Files: |  1-s2.0-S0968089625003670-main.pdf (10,88 MB)
MD5: F388D7DA92006627C527B1F61F8DA4E7
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.02 - Review Article |
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| Organization: | FKKT - Faculty of Chemistry and Chemical Engineering
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| 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. |
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| Keywords: | machine learning, artificial intelligence, antiviral compounds, biological activity |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 28.07.2025 |
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| Article acceptance date: | 29.09.2025 |
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| Publication date: | 30.09.2025 |
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| Publisher: | Elsevier |
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| Year of publishing: | 2026 |
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| Number of pages: | 36 str. |
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| Numbering: | Vol. 132, [Article no.] 118426 |
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| PID: | 20.500.12556/DKUM-95737  |
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| UDC: | 577 |
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| ISSN on article: | 1464-3391 |
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| COBISS.SI-ID: | 253578755 |
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| DOI: | 10.1016/j.bmc.2025.118426 |
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| Copyright: | © 2025 The Authors |
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| Publication date in DKUM: | 17.10.2025 |
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| Views: | 239 |
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| Downloads: | 17 |
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| Metadata: |    |
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| Categories: | Misc.
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