Development of Predictive Antioxidant Models for 1,3,4-Oxadiazoles by Quantitative Structure Activity Relationship

Abstract

The free radical scavenging properties of 1,3,4-oxadiazoles have been explored by theapplication of quantitative structure activity relationship (QSAR) studies. The entire data set of theoxadiazole derivatives were minimized and subsequently optimized at the density functional theory (DFT)level in combination with the Becke's three-parameter Lee-Yang-Parr (B3LYP) hybrid functional and 6-311G* basis set. Kennard Stone algorithm was employed in data division into training and test sets. Thetraining set was employed in QSAR model development by genetic function algorithm (GFA), while the testset was used to validate the developed models. The applicability domain of the developed model wasaccessed by the leverage approach. The variation inflation factor, degree of contribution and mean effectof each descriptor were calculated. Quantum chemical and molecular descriptors were generated for eachmolecule in the data set. Five predictive models that met all the requirements for acceptability with goodvalidation results were developed. The best of the five models gave the following validation results: 𝑅 =0.944, 𝑅2 = 0.891, 𝑄2(𝑅2𝐶𝑉) = 0.831, 𝑅2𝑝𝑟𝑒𝑑 = 0.858 and c𝑅𝑝2 = 0.810 𝑠 = 0.114, rmsep = 0.121 . The QSARanalysis revealed that the sum of e-state descriptors of strength for potential hydrogen bonds of pathlength 9 (SHBint9) and topological radius (topoRadius) are the most crucial descriptors that influence thefree radical scavenging activities of 1,3,4-oxadiazole derivatives.