{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,30]],"date-time":"2025-11-30T12:47:12Z","timestamp":1764506832198,"version":"3.46.0"},"reference-count":27,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,6,29]],"date-time":"2025-06-29T00:00:00Z","timestamp":1751155200000},"content-version":"vor","delay-in-days":28,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Cellular Biochemistry"],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Generating new and efficient drugs through machine learning\u2010assisted quantitative structure\u2013activity relationships (ML\u2010QSAR) has become a promising strategy for addressing multidrug\u2010resistant gram\u2010negative bacterial infections. We developed robust ML\u2010QSAR models using Genetic Function Approximation (GFA), Support Vector Machine (SVM), and Artificial Neural Network (ANN) methods to predict the activity of experimentally known quinoline\u2010based MsbA inhibitors, aiming to create more effective antibacterial drugs. The ML models were built using eight significant molecular descriptors: B09[N\u2010Cl], CATS3D_04_AA, F06[N\u2010O], G2i, molecular weight (MW), Mor04p, VE1sign_B(s), and VE1sign_Dz(i), along with 279 molecular fingerprints to predict the MsbA inhibition activity of quinoline\u2010based compounds. The molecular descriptor\u2010based SVM model achieved an R\u00b2 of 0.9891 and a q\u00b2 cross\u2010validation correlation of 0.9388. In contrast, the molecular fingerprint\u2010based SVM model had an R\u00b2 of 0.9981 and a q\u00b2 cross\u2010validation correlation of 0.7584, making it the best\u2010performing model. The robustness of these developed models was further validated through various internal, external, and applicability domain analyses. The most active compounds identified in this data set, compounds 31 and 40, were subsequently used to generate 62 new quinoline\u2010based compounds. Additionally, three modelled quinoline\u2010based inhibitors, M28, N7, and N23, demonstrated excellent bioactivity, binding affinity, and pharmacokinetic profiles. To support further research, the fingerprint\u2010based ML\u2010QSAR model is available as a web application, MsbA\u2010Pred (\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/msba-mohan-amrita.streamlit.app\/\">https:\/\/msba-mohan-amrita.streamlit.app\/<\/jats:ext-link>\n                    ), which allows users to predict MsbA inhibitory activity from any device.\n                  <\/jats:p>","DOI":"10.1002\/jcb.70049","type":"journal-article","created":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T01:45:08Z","timestamp":1751247908000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning Models and Structure\u2010Based Antibacterial Drug Discovery of the Key ABC Transporter Maltose\u2010Binding Protein A"],"prefix":"10.1002","volume":"126","author":[{"given":"Anupama","family":"Binoy","sequence":"first","affiliation":[{"name":"Bioinformatics and Computational Biology Lab, Amrita School for Nanosciences and Molecular Medicine Amrita Vishwa Vidyapeetham Kochi Kerala India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7013-7323","authenticated-orcid":false,"given":"Ratul","family":"Bhowmik","sequence":"additional","affiliation":[{"name":"Bioinformatics and Computational Biology Lab, Amrita School for Nanosciences and Molecular Medicine Amrita Vishwa Vidyapeetham Kochi Kerala India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Preena S.","family":"Parvathy","sequence":"additional","affiliation":[{"name":"Bioinformatics and Computational Biology Lab, Amrita School for Nanosciences and Molecular Medicine Amrita Vishwa Vidyapeetham Kochi Kerala India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2056-7189","authenticated-orcid":false,"given":"C.","family":"Gopi Mohan","sequence":"additional","affiliation":[{"name":"Bioinformatics and Computational Biology Lab, Amrita School for Nanosciences and Molecular Medicine Amrita Vishwa Vidyapeetham Kochi Kerala 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