Sayiner, Hakan Sezgin, Abdalrahm, Afaf A S, Basaran, Murat A, Kovalishyn, Vasyl, Kandemirli, FatmaSayiner H.S., Abdalrahm A.A.S., Başaran M.A., Kovalishyn V., Kandemirli F.Sayiner, HS, Abdalrahm, AAS, Basaran, MA, Kovalishyn, V, Kandemirli, F2023-05-092023-05-092018-04-072018-05-012018.01.011573-4064https://hdl.handle.net/20.500.12597/14287Acinetobacter is a Gram-negative, catalase-positive, oxidase-negative, non-motile, and no fermenting bacteria.In this study, some of the electronic and molecular properties, such as the highest occupied molecular orbital energy (EHOMO), lowest unoccupied molecular orbital energy (ELUMO), the energy gap between EHOMO and ELUMO, 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.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.Artificial Neural Networks obtained accuracies in the range of 83-100% (for active/inactive classifications) and q2=0.63 for regression.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 q2=0.62 for regression model and overall accuracy 70-95 % for classification models.Background: 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 (EHOMO), lowest unoccupied molecular orbital energy (ELUMO), the energy gap between EHOMO and ELUMO, 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 q2=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 q2=0.62 for regression model and overall accuracy 70-95 % for classification models.falseA. baumanniiDFTE. coliQSARartificial neural networksdragongram-negative bacteriaA. baumannii | Artificial neural networks | DFT | Dragon | E. coli | Gram-negative bacteria | QSARJournal Article10.2174/157340641366617100212440810.2174/15734064136661710021244082-s2.0-85046624122WOS:00042955440000628969576253268141875-6638