Combining DFT and QSAR computation for predicting the soil sorption coefficients of substituted phenols and anilines

The soil sorption coefficient (koc) is a key physicochemical parameter to assess the environmental risk of organic compounds. to predict the soil sorption coefficient in the more effective and economical way,here the qsar model is applied to the set of 42 substituted phenols and anilines. this study was conducted using the principal component analysis (pca),multiple linear regression (mlr),nonlinear regression (rnlm) and artificial neural network (ann). we propose a quantitative model according to these analyses,and we interpreted the soil sorption coefficient of the compounds based on the multivariate statistical analysis. density functional theory (dft) with beck's three parameter hybrid functional using the lyp correlation functional (b3lyp/6-31g(d)) calculations have been carried out in order to get insights into the structure chemical and property information for the study compounds. this study shows that the mra and mnlr have served to predict the soil sorption coefficient,but compared to the results of the ann model,we conclude that the predictions fulfilled by the latter are more effective and better than other models.