Prediction of octanol–water partition coefficients of organic chemicals by QSAR models

Abstract

A quantitative structure–activity relationship was developed using the partial least square (PLS), kernel PLS (KPLS) and Levenberg–Marquardt artificial neural network (L-M ANN) approach for chemometrics study. The logarithm octanol–water partition coefficients (logKow) of 47 organic chemicals in effluents were obtained by reversed-phase high-performance liquid chromatography. A genetic algorithm (GA) was employed as factor selection procedure for PLS and KPLS modeling. For comparison, GA-PLS descriptors were selected for L-M ANN. A model with low prediction errors and good correlation coefficients was obtained by L-M ANN.