Prediction of different antibacterial activity in a new set of formyl hydroxyamino derivatives with potent action on peptide deformylase using structural information

Abstract

Due to the essential role of peptide deformylase (PDF) at the bacterial growth cycle, it is a noteworthy target for developing a novel antibacterial agent. In the current study, the antibacterial activities of a set of 44 new structures of formyl hydroxyamino derivatives as PDF inhibitors were quantified using quantitative structure–activity relationship (QSAR). Artificial neural networks (ANN) were used as a chemometrics tool for QSAR modeling. Three quantitative models were suggested to relate the chemical structural features of the formyl hydroxyamino derivatives to their antibacterial activities (pIC50) against Staphylococcus aureus, methicillin-susceptible S. aureus (MSSA), and methicillin-resistant S. aureus (MRSA) peptide deformylase. The sufficiency of the model for prediction of the antibacterial activities of the desired PDF inhibitor compounds against S. aureus, MSSA, and MRSA was statistically demonstrated according to the validation parameters such as coefficient of determination (R2), mean square error (MSE) in training, validation, and prediction sets, and also using applicability domain (AD) and randomization test.