Experimental and DFT Computational Insights on the Adsorption of Selected Pharmaceuticals of Emerging Concern from Water Systems onto Magnetically Modified Biochar

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

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.