The use of simple structural parameters of organic compounds to assess their PUF-air partition coefficients

A novel approach is introduced for the reliable prediction of PUF-air partition coefficients of organic compounds, which can determine the environmental fate of organic compounds during interactions with air, soil, and water. The biggest accessible measured data of PUF-air partition coefficients for 170 chemicals are used to develop and test the novel model. In comparison to available quantitative structure-property relationship (QSPR) methods for the prediction of PUF-air partition coefficients that need complex descriptors, the here used descriptors are simpler. The assessed various statistical factors of the simple method containing 147 (training) and 23 (test) organic compounds can verify the external and internal cross-validations. Various statistical parameters confirm the high reliability of the novel model as compared with the outputs of complex multiple linear regression (MLR), artificial neural network (ANN) and support vector machine (SVM) methods. The values of R-squared (R²), and root mean square error (RMSE) of the new model are for training/test sets are 0.924/0.894 and 0.374/0.318, respectively. Meanwhile, R² and RMSE values for three comparative models training/test sets are (i) MLR: 0.848/0.670 (R²) and 0.531/0.573 (RMSE); (ii) ANN: 0.902/0.664 (R²) and 0.425/0.560 (RMSE); (iii) SVM: 0.935/0.794 (R²) and 0.351/0.419 (RMSE). Thus, the new model the simplest approach with higher reliability in comparison to the best available methods.