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The Quantum Chemical and QSAR Studies on Acinetobacter Baumannii Oxphos Inhibitors     
Yazarlar
Hakan Sezgin Sayiner
Adıyaman Üniversitesi, Türkiye
Afaf Abdalrahm
Murat Alper Başaran
Alanya Alaaddin Keykubat Üniversitesi, Türkiye
Vasyl Kovalishyn
Prof. Dr. Fatma KANDEMİRLİ Prof. Dr. Fatma KANDEMİRLİ
Kastamonu Üniversitesi, Türkiye
Özet
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.
Anahtar Kelimeler
A. baumannii | Artificial neural networks | DFT | Dragon | E. coli | Gram-negative bacteria | QSAR
Makale Türü Özgün Makale
Makale Alt Türü SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale
Dergi Adı Medicinal Chemistry
Dergi ISSN 1573-4064
Dergi Tarandığı Indeksler SCI
Makale Dili İngilizce
Basım Tarihi 01-2018
Cilt No 14
Sayı 3
Sayfalar 253 / 268
Doi Numarası 10.2174/1573406413666171002124408