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

The 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 . The generated GRNN in this work has a correlation coefficient of .

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