Deep learning approach to the discovery of novel bisbenzazole derivatives for antimicrobial effect

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dc.authoridYucel, Mehmet Ali/0000-0003-2880-7992
dc.authorwosidYucel, Mehmet Ali/ABB-8154-2022
dc.contributor.authorBarcin, Tunga
dc.contributor.authorYucel, Mehmet Ali
dc.contributor.authorErsan, Ronak Haj
dc.contributor.authorAlagoz, Mehmet Abdullah
dc.contributor.authorDogen, Aylin
dc.contributor.authorBurmaoglu, Serdar
dc.contributor.authorAlgul, Oztekin
dc.date.accessioned2024-08-04T20:54:46Z
dc.date.available2024-08-04T20:54:46Z
dc.date.issued2024
dc.departmentİnönü Üniversitesien_US
dc.description.abstractBecause of the growing bacterial resistance to antibiotics, the discovery of new antibiotics is critical. The search for new antimicrobial drugs that are effective in treating new and existing microbial diseases is arduous and timeconsuming. Deep learning (DL) can help find potential candidates resulting in a more efficient, and cost-effective, and it is more useful on large datasets than other algorithms.Our research team focused on developing an effective DL workflow for discovering new antimicrobial agents. Our group has previously synthesized and tested bisbenzazole structures with various linkers for a variety of pharmacological activities. Antimicrobial activities of bisbenzazole compounds have been also reported in the literature. Deep Neural Networks (DNN) were used to predict the activity of all bisbenzazole compounds synthesized by our group against Staphylococcus aureus and Candida albicans. DNN successfully predicted compounds 16, 17, and 30 out of six molecules (11, 16, 17, 29, 30, and 33) with activity results of 31.25 mu g /mL or better results based on in vitro studies. Compounds 13 and 15 out of four molecules (13, 15, 29, and 30) for C. albicans were successfully predicted. Molecular modeling studies were also carried out, and the compounds' docking scores agreed with the DNN models and in vitro antimicrobial activity results. Finally, this workflow, which includes deep learning, molecular docking, and in vitro studies, is a dependable and efficient way of discovering new antimicrobial agents for S. aureus and C. albicans.en_US
dc.identifier.doi10.1016/j.molstruc.2023.136668
dc.identifier.issn0022-2860
dc.identifier.issn1872-8014
dc.identifier.scopus2-s2.0-85173175167en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1016/j.molstruc.2023.136668
dc.identifier.urihttps://hdl.handle.net/11616/101603
dc.identifier.volume1295en_US
dc.identifier.wosWOS:001089079600001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofJournal of Molecular Structureen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBenzazolesen_US
dc.subjectDeep learningen_US
dc.subjectDeep neural networksen_US
dc.subjectAntimicrobial activityen_US
dc.subjectStaphylococcus aureusen_US
dc.subjectCandida albicansen_US
dc.titleDeep learning approach to the discovery of novel bisbenzazole derivatives for antimicrobial effecten_US
dc.typeArticleen_US

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