Exploring QSTR analysis of the toxicity of phenols and thiophenols using machine learning methods

Abstract

There is an increasing need for the rapid safety assessment of chemicals by both industries and regulatory agencies throughout the world. In silico techniques are practical alternatives in the environmental hazard assessment. In this background, quantitative structure–toxicity relationship (QSTR) analysis has been performed on toxicity of phenols and thiophenols to Photobacterium phosphoreum. The techniques of classification and regression trees (CART) and least squares support vector regressions (LS-SVR) were applied successfully as variable selection and mapping tools, respectively. Four descriptors selected by the CART technique have been used as inputs of the LS-SVR for prediction of toxicities. The best model explains 91.8% leave-one-out predicted variance and 93.0% external predicted variance. The predictive performance of the CART-LS-SVR model was significantly better than the previous reported models based on CoMFA/CoMSIA and stepwise MLR techniques, suggesting that the present methodology may be useful to predict of toxicity, safety and risk assessment of chemicals.