The quantum chemical and QSAR studies on acinetobacter baumannii oxphos inhibitors

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dc.contributor.authorSayıner, Hakan Sezgin
dc.contributor.authorAbdalrahm, Afaf A. S.
dc.contributor.authorBaşaran, Murat A.
dc.contributor.authorKovalishyn, Vasyl
dc.contributor.authorKandemirli, Fatma
dc.date.accessioned2021-02-19T21:16:07Z
dc.date.available2021-02-19T21:16:07Z
dc.date.issued2018
dc.departmentALKÜ
dc.descriptionsayiner, hakan sezgin/0000-0002-4693-3784; Kovalishyn, Vasyl/0000-0002-9352-7332; Basaran, Murat Alper/0000-0001-9887-5531
dc.description.abstractBackground: Acinetobacter is a Gram-negative, catalase-positive, oxidase-negative, non-motile, and no fermenting bacteria. Objective: In this study, some of the electronic and molecular properties, such as the highest occupied molecular orbital energy (E-HOMO), lowest unoccupied molecular orbital energy (ELUMO), the energy gap between E-HOMO and E-LUMO, Mulliken atomic charges, bond lengths, of molecules having impact on antibacterial activity against A. baumannii were studied. In addition, calculations of some QSAR descriptors such as global hardness, softness, electronegativity, chemical potential, global electrophilicity, nucleofugality, electrofugality were performed. Method: The descriptors having impact on antibacterial activity against A. baumannii have been investigated based on the usage of 29 compounds employing two statistical methods called Linear Regression and Artificial Neural Networks. Results: Artificial Neural Networks obtained accuracies in the range of 83-100% (for active/inactive classifications) and q(2)=0.63 for regression. Conclusion: Three ANN models were built using various types of descriptors with publicly available structurally diverse data set. QSAR methodologies used Artificial Neural Networks. The predictive ability of the models was tested with cross-validation procedure, giving a q(2)=0.62 for regression model and overall accuracy 70-95 % for classification models.
dc.identifier.doi10.2174/1573406413666171002124408
dc.identifier.endpage268en_US
dc.identifier.issn1573-4064
dc.identifier.issn1875-6638
dc.identifier.issue3en_US
dc.identifier.pmid28969576
dc.identifier.scopusqualityQ3
dc.identifier.startpage253en_US
dc.identifier.urihttps://doi.org/10.2174/1573406413666171002124408
dc.identifier.urihttps://hdl.handle.net/20.500.12868/248
dc.identifier.volume14en_US
dc.identifier.wosWOS:000429554400006
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthor0-belirlenecek
dc.language.isoen
dc.publisherBentham Science Publ Ltd
dc.relation.ispartofMedicinal Chemistry
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectA. baumannii
dc.subjectQSAR
dc.subjectartificial neural networks
dc.subjectDFT
dc.subjectdragon
dc.subjectgram-negative bacteria
dc.subjectE. coli
dc.titleThe quantum chemical and QSAR studies on acinetobacter baumannii oxphos inhibitors
dc.typeArticle

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