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

• Published in: Journal of the Turkish Chemical Society, Section A: Chemistry
• Main Author: Alias A.N.; Zabidi Z.M.
• Format: Article
• Language: English
• Published: Turkish Chemical Society 2022
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137379752&doi=10.18596%2fjotcsa.1083840&partnerID=40&md5=42edaa8b3b8e208bb8998d60e801118e

Nitrobenzene derivatives are organic compounds that have been widely synthesized and used in chemical industries such as the polymer industry, lumber preservatives, textile industry, pesticides, and warlike weapons industry. The rapid growth of nitrobenzene derivatives in the industry requires research into the effects of toxicity in the environment. 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 of the benzene derivatives used second order hyper-polarizability and COs area, which satisfied the statistical measures. Second order hyperpolarizability shows the best QSAR model with the value of R2 = 89.493%, r2 = 68.7% and rcv 2 = 87.52%. We also found that the substitution of functional group in the nitrobenzene derivative for second order hyperpolarizability has the same sequence which was the γ ortho < γ meta < γ para. These has made that the second order hyperpolarizability was the best descriptors for QSAR model. © 2022, Turkish Chemical Society. All rights reserved.