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.
Authors’ contributions
This work was equally contributed by all writers.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
This study was not based on any data.