QSPR Modeling with Curvilinear Regression on the Reverse Entropy Indices for the Prediction of Physicochemical Properties of Benzene Derivatives
Muhammad Naeem, Abdul Rauf, Muhammad Shahzad Akhtar, Zafar Iqbal
Abstract
Reverse entropies are the molecular descriptors that describe the structures of chemical compounds. They are used in isomer discrimination, structure-property relationship, and structure-activity relations. In this study, the QSPR models were designed using the reverse degree-based entropies to predict the physical properties of benzene derivatives. The relationship analyses between the physicochemical properties and the reverse entropies were done by using the curvilinear regression method. A Maple software based algorithm was designed to make the computation of reverse degree-based entropies easy. Analysis was performed using SPSS software. We analyzed that physical properties such as critical pressure, critical temperature, critical volume, Gibb’s energy, LogP, molar refractivity, and Henry’s law can be estimated by the QSPR model using reverse entropies. All the results were highly positive and significant.