{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T03:27:00Z","timestamp":1771903620384,"version":"3.50.1"},"reference-count":108,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T00:00:00Z","timestamp":1737676800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Foundation for Science and Technology\/the Ministry of Science, Technology and Higher Education of the Government of Portugal","award":["UIDB\/50006\/2020"],"award-info":[{"award-number":["UIDB\/50006\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>Antibacterial drugs (commonly known as antibiotics) are essential for eradicating bacterial infections. Nowadays, antibacterial discovery has become an imperative need due to the lack of efficacious antibiotics, the ever-increasing development of multi-drug resistance (MDR), and the withdrawal of many pharmaceutical industries from antibacterial discovery programs. Currently, drug discovery is widely recognized as a multi-objective optimization problem where computational approaches could play a pivotal role, enabling the identification of novel and versatile antibacterial agents. Yet, tackling complex phenomena such as the multi-genic nature of bacterial infections and MDR is a major disadvantage of most of the modern computational methods. To the best of our knowledge, perturbation-theory machine learning (PTML) appears to be the only computational approach capable of overcoming the aforementioned limitation. The present review discusses PTML modeling as the most suitable cutting-edge computational approach for multi-objective optimization in antibacterial discovery. In this sense, we focus our attention on the development and application of PTML models for the prediction and\/or design of multi-target (multi-protein or multi-strain) antibacterial inhibitors in the context of small organic molecules, peptide design, and metal-containing nanoparticles. Additionally, we highlight future applications of PTML modeling in the context of novel drug-like chemotypes with multi-protein and\/or multi-strain antibacterial activity.<\/jats:p>","DOI":"10.3390\/app15031166","type":"journal-article","created":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T04:32:22Z","timestamp":1737693142000},"page":"1166","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Perturbation-Theory Machine Learning for Multi-Objective Antibacterial Discovery: Current Status and Future Perspectives"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1928-853X","authenticated-orcid":false,"given":"Valeria V.","family":"Kleandrova","sequence":"first","affiliation":[{"name":"LAQV@REQUIMTE\/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3375-8670","authenticated-orcid":false,"given":"M. Nat\u00e1lia D. S.","family":"Cordeiro","sequence":"additional","affiliation":[{"name":"LAQV@REQUIMTE\/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9544-9016","authenticated-orcid":false,"given":"Alejandro","family":"Speck-Planche","sequence":"additional","affiliation":[{"name":"LAQV@REQUIMTE\/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2221","DOI":"10.1016\/S0140-6736(22)02185-7","article-title":"Global mortality associated with 33 bacterial pathogens in 2019: A systematic analysis for the Global Burden of Disease Study 2019","volume":"400","author":"Collaborators","year":"2022","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"415","DOI":"10.2147\/IDR.S287792","article-title":"Encouraging the Development of New Antibiotics: Are Financial Incentives the Right Way Forward? 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