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Abstract:The standard enthalpy of formation of 1115 compounds from all chemical groups, were predicted using genetic algorithm-based multivariate linear regression (GA-MLR). The obtained multivariate linear five descriptors model by GA-MLR has correlation coefficient ( 9830 . 0 2 = R ). All molecular descriptors which have entered in this model are calculated from chemical structure of any molecule. As a result, application of this model for any compound is easy and accurate.

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 R 2 ¼ 0:821. The generated GRNN in this work has a correlation coefficient of R 2 ¼ 0:98.

In the present work, a quantitative structure−property relationship (QSPR) study is performed to predict the aniline point temperature of pure hydrocarbon components. As a powerful tool, genetic algorithm-based multivariate linear regression (GA-MLR) is applied to select most statistically effective molecular descriptors on the aniline point temperature of pure hydrocarbon components. Also, a three-layer feed forward neural network (FFNN) is constructed to consider the nonlinear behavior of appearing molecular descriptors in GA-MLR result. The obtained results show that the constructed FFNN can accurately predict the aniline point temperature of pure hydrocarbon components.