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

Year 2019, Volume: 6 Issue: 2, 103 - 114, 15.06.2019

Ikechukwu Alisi , Adamu Uzairu Stephen Eyije Abechi , Suleiman Ola Idris

https://doi.org/10.18596/jotcsa.406207

Cited By: 4

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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