Prediction of Solubility of Nonionic Solutes in Anionic Micelle (SDS) Using a QSPR Model

Abstract

AbstractA Quantitative structure–property relationships analysis has been applied to a series of chemical compounds which were of interest because of their roles in environmental samples. The incorporation neutral solute (log Ks) in anionic micelle (SDS) has been predicted by using molecular structural descriptors for a heterogeneous set of 62 neutral solutes with a range of more than 4log units. Two linear correlating models, Multiple Linear Regression (MLR) and Partial Least Squares (PLS) regression methods were used. The stepwise MLR of SPSS software was used for the selection of the variables. After variables selection, MLR and PLS methods used leave‐one‐out cross‐validation for building the regression models. Appropriate models with low standard errors and high correlation coefficients were selected. The results showed that both MLR and PLS methods could model the relationship between solubility and their electronic and thermodynamic descriptors perfectly. The predictive quality of the QSPR models were tested for an external prediction set of 11 compounds randomly chosen from 62 compounds. The squared regression coefficients of prediction for the MLR and PLS regression methods were 0.9679 and 0.9728 respectively.