Developing a support vector machine based QSPR model for prediction of half-life of some herbicides

Abstract

The half-life (t1/2) of 58 herbicides were modeled by quantitative structure–property relationship (QSPR) based molecular structure descriptors. After calculation and the screening of a large number of molecular descriptors, the most relevant those ones selected by stepwise multiple linear regression were used for developing linear and nonlinear models which developed by using multiple linear regression and support vector machine, respectively. Comparison between statistical parameters of linear and nonlinear models indicates the suitability of SVM over MLR model for predicting the half-life of herbicides. The statistical parameters of R2 and standard error for training set of SVM model were; 0.96 and 0.087, respectively, and were 0.93 and 0.092 for the test set. The SVM model was evaluated by leave one out cross validation test, which its result indicates the robustness and predictability of the model. The established SVM model was used for predicting the half-life of other herbicides that are located in the applicability domain of model that were determined via leverage approach.The results of this study indicate that the relationship among selected molecular descriptors and herbicide's half-life is non-linear. These results emphases that the process of degradation of herbicides in the environment is very complex and can be affected by various environmental and structural features, therefore simple linear model cannot be able to successfully predict it.