In this investigation, based on the general interaction properties function (GIPF) family descriptors computed at the B3LYP/STO-3G level using the software Gaussian98, a significant QSPR/QSAR correlations for the physicochemical properties and biological activities of benzenoid hydrocarbons have been extensively constructed by using multiple linear regression (MLR) analysis, following the guidelines for development and validation of regression models. The regression models were interpreted in terms of selected descriptors and demonstrated these properties/acticities depend on forces that are electrostatic in nature. Although for some characterizations, few molecules have data but more models have one descriptors and ratio between number of molecules and descriptors do not exceed from 4 and their R2 are between 0.66 and 0.99. Based on these models, we predicted properties for molecules that have no data and whereas our models are based on descriptors that are calculated from computed molecular surface electrostatic potentials and resulted with multiple linear regression (MLR) analysis, these make our research distinct from other works about benzenoid hydrocarbons.
