QSPR Studies for Solubility Parameter by Means of Genetic Algorithm‐Based Multivariate Linear Regression and Generalized Regression Neural Network

Abstract

AbstractThe solubility parameters of 1228 solvents, from all the chemical groups, were predicted using Genetic Algorithm‐Based Multivariate Linear Regression (GA‐MLR) and Generalized Function Approximation Neural Network (GRNN). GA‐MLR was used to select the molecular descriptors, as inputs for GRNN. The obtained multivariate linear seven descriptors model by GA‐MLR had a correlation coefficient of $\rm{ R^2 = 0.821}$. The generated GRNN in this work has a correlation coefficient of $\rm{ R^2 = 0.98}$.