{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T11:51:30Z","timestamp":1768477890624,"version":"3.49.0"},"reference-count":99,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T00:00:00Z","timestamp":1738281600000},"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":["Pharmaceuticals"],"abstract":"<jats:p>Background\/Objectives: Infectious diseases caused by Staphylococcus aureus (S. aureus) have become alarming health issues worldwide due to the ever-increasing emergence of multidrug resistance. In silico approaches can accelerate the identification and\/or design of versatile antibacterial chemicals with the ability to target multiple S. aureus strains with varying degrees of drug resistance. Here, we develop a perturbation theory machine learning model based on a multilayer perceptron neural network (PTML-MLP) for the prediction and design of versatile virtual inhibitors against S. aureus strains. Methods: To develop the PTML-MLP model, chemical and biological data associated with antibacterial activity against S. aureus strains were retrieved from the ChEMBL database. We applied the Box\u2013Jenkins approach to convert the topological indices into multi-label graph-theoretical indices; the latter were used as inputs for the creation of the PTML-MLP model. Results: The PTML-MLP model exhibited accuracy higher than 80% in both training and test sets. The physicochemical and structural interpretation of the PTML-MLP model was performed through the fragment-based topological design (FBTD) approach. Such interpretations permitted the analysis of different molecular fragments with favorable contributions to the multi-strain antibacterial activity and the design of four new drug-like molecules using different fragments as building blocks. The designed molecules were predicted\/confirmed by our PTML model as multi-strain inhibitors of diverse S. aureus strains, thus representing promising chemotypes to be considered for future synthesis and biological testing of versatile anti-S. aureus agents. Conclusions: This work envisages promising applications of PTML modeling for early antibacterial drug discovery and related antimicrobial research areas.<\/jats:p>","DOI":"10.3390\/ph18020196","type":"journal-article","created":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T05:08:26Z","timestamp":1738300106000},"page":"196","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["In Silico Approach for Antibacterial Discovery: PTML Modeling of Virtual Multi-Strain Inhibitors Against Staphylococcus aureus"],"prefix":"10.3390","volume":"18","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"}]},{"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":[[2025,1,31]]},"reference":[{"key":"ref_1","unstructured":"Collaborators GBDAR (2022). Global mortality associated with 33 bacterial pathogens in 2019: A systematic analysis for the Global Burden of Disease Study 2019. 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