The quantum chemical and QSAR studies on acinetobacter baumannii oxphos inhibitors
[ X ]
Tarih
2018
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Bentham Science Publ Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
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 (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.
Açıklama
sayiner, hakan sezgin/0000-0002-4693-3784; Kovalishyn, Vasyl/0000-0002-9352-7332; Basaran, Murat Alper/0000-0001-9887-5531
Anahtar Kelimeler
A. baumannii, QSAR, artificial neural networks, DFT, dragon, gram-negative bacteria, E. coli
Kaynak
Medicinal Chemistry
WoS Q Değeri
N/A
Scopus Q Değeri
Q3
Cilt
14
Sayı
3
