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.