Prediction of gas to water solvation enthalpy of organic compounds using support vector machine

Abstract

Quantitative structure–property relationship (QSPR) models were developed to predict gas to water solvation enthalpy (ΔHSolv) of various organic compounds based on physico-chemical descriptors. Six molecular descriptors selected by genetic algorithm (GA) feature selection technique were used as inputs to perform partial least squares (PLS), artificial neural network (ANN) and support vector machine (SVM) studies. The correlation coefficient (R) between experimental and predicted solvation enthalpy for prediction sets by PLS, ANN and SVM are 0.935, 0.990 and 0.993, respectively. The results demonstrated that 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. This indicates that SVM can be used as an alternative modeling tool for quantitative structure–property relationship (QSPR) studies.