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 the application of quantitative structure activity relationship (QSAR) studies. The entire data set of the oxadiazole derivatives were minimized and subsequently optimized at the density functional theory (DFT) level in combination with the Becke's three-parameter Lee-Yang-Parr hybrid functional (B3LYP) hybrid functional and 6-311G* basis set. Kennard Stone algorithm was employed in data division into training and test sets. The training set were employed in QSAR model development by genetic function algorithm (GFA), while the test set were used to validate the developed models. The applicability domain of the developed model was accessed by the leverage approach. The varation inflation factor, degree of contribution and mean effect of each descriptor were calculated.  Quantum chemical and molecular descriptors were generated for each molecule in the data set. Five predictive models that met all the requirements for acceptability with good validation results were developed. The best of the five models gave the following validation results: ,   ,  and ^c   ,  rmsep  . The QSAR analysis revealed that the sum of e-state descriptors of strength for potential hydrogen bonds of path length 9 (SHBint9) and topological radius (topoRadius) are the most crucial descriptors that influence the free radical scavenging activities of 1,3,4-oxadiazole derivatives.

Keywords

• Antioxidants
• 1 3 4-OXadiazoles
• Descriptors
• Model validation
• QSAR

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