QSPR-based model extrapolation prediction of enthalpy of solvation

Abstract

Enthalpy of solvation (ΔHsolv) is an important thermodynamic parameter in chemical, biological and environmental science. In the current submission a quantitative structure–property relationship (QSPR) model was developed for a large data set of 6106 enthalpies of solvation in 68 solvents, by applying generalized regression neural network (GRNN) using Dragon descriptors for both the solutes and solvents. The optimal GRNN model with the smoothing factor σ = 0.16 was based on 3082 enthalpies of solvation in 34 solvents in the training set, and validated with 3024 enthalpies of solvation in other 34 solvents in the test set. The optimal GRNN model has correlation coefficients of 0.978 and 0.943, and root mean square errors of 4.153 and 6.088 kJ/mol for the training and test sets. In predicting the enthalpies of solvation, the optimal GRNN model is comparable to other published QSPR models reported in the literature, however, the number of ΔHsolv in the test set is 4 times more than that from the latest reported literature study predicting enthalpies of solvation (3024 to 632).