QSAR Studies on Nitrobenzene Derivatives using Hyperpolarizability and Conductor like Screening model as Molecular Descriptors

Year 2022, Volume: 9 Issue: 3, 953 - 968, 31.08.2022

Ahmad Nazib Alias , Zubainun Mohamed Zabidi

https://doi.org/10.18596/jotcsa.1083840

Abstract

Quantitative structure-activity relationship (QSAR) models were useful in understanding how chemical structure relates to the toxicology of chemicals. In the present study, we report quantum molecular descriptors using conductor like screening model (COs) area, the linear polarizability, first and second order hyperpolarizability for modelling the toxicology of the nitro substituent on the benzene ring. All the molecular descriptors were performed using semi-empirical PM6 approaches. The QSAR model was developed using stepwise multiple linear regression. We found that the stable QSAR modelling of toxicology benzene derivatives used second order hyper-polarizability and COs area, which satisfied the statistical measures. The second order hyperpolarizability shows the best QSAR model. We also discovered that the nitrobenzene derivative’s substitutional functional group has a significant effect on the quantum molecular descriptors, which reflect the QSAR model.

Keywords

nitrobenzene, toxicity, QSAR, quantum molecular descriptors, high order hyperpolarizability, COs Area

Thanks

The authors would like to thank Dr James J. P. Stewart from MOPAC Inc. for his permission to use the MOPAC software, and UiTM’s Department of Infostructure for the MiniTab software usage permission. Additionally, the authors wish to express gratitude to Universiti Teknologi MARA for financial assistance.

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