QSPR studies for predicting gas to acetone and gas to acetonitrile solvation enthalpies using support vector machine

Abstract

Quantitative structure–properties relationship (QSPR) has been applied to modelling and predicting the gas to acetone and gas to acetonitrile solvation enthalpies (ΔHSolv) of organic compounds using partial least squares (PLS), artificial neural network (ANN) and support vector machine (SVM) techniques. Two different datasets were assessed. The first one contained a set of gas to acetone enthalpy of solvation data of 68 different organic compounds while the second one included a total of 69 experimental data points for the enthalpy of solvation in acetonitrile. Genetic algorithm (GA) was used to search the descriptor space and select the descriptors responsible for property. After the variable selection, PLS, ANN and SVM were utilized to construct linear and non-linear QSPR models. Our study demonstrates that the reliance of chemical properties on solvation enthalpies is a nonlinear phenomenon and that PLS method is not capable to model it. The results obtained, illustrate that, for both datasets, the calculated ΔHSolv values by SVM were in good agreement with the experimental ones, and the performances of the SVM models were comparable or superior to those of PLS and ANN ones.