Prediction of solubility parameters using partial least square regression.

Abstract

The total solubility parameter (delta) values were effectively predicted by using computed molecular descriptors and multivariate partial least squares (PLS) statistics. The molecular descriptors in the derived models included heat of formation, dipole moment, molar refractivity, solvent-accessible surface area (SA), surface-bounded molecular volume (SV), unsaturated index (Ui), and hydrophilic index (Hy). The values of these descriptors were computed by the use of HyperChem 7.5, QSPR Properties module in HyperChem 7.5, and Dragon Web version. The other two descriptors, hydrogen bonding donor (HD), and hydrogen bond-forming ability (HB) were also included in the models. The final reduced model of the whole data set had R(2) of 0.853, Q(2) of 0.813, root mean squared error from the cross-validation of the training set (RMSEcv(tr)) of 2.096 and RMSE of calibration (RMSE(tr)) of 1.857. No outlier was observed from this data set of 51 diverse compounds. Additionally, the predictive power of the developed model was comparable to the well recognized systems of Hansen, van Krevelen and Hoftyzer, and Hoy.