Structure-toxicity relationships for phenols and anilines towards Chlorella vulgaris using quantum chemical descriptors and statistical methods.

Abstract

Quantitative structure–toxicity relationship (QSTR) models are useful to understand how chemical structure relates to the toxicity of natural and synthetic chemicals. The chemical structures of 67 phenols and anilines have been characterized by electronic and physic-chemical descriptors. 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. The statistical quality of the MLR and MNLR models was found to be efficient for the predicting of the toxicity, but when compared to the obtained results by ANN model, we realized that the predictions achieved by this latter one were more effective. The results indicated that the developed models could produce satisfactory predictive results for the four different toxicity endpoints with high squared correlation coefficients (R² ). Leave-one-out cross validation, external validation, Y-randomized validation and application domain analysis demonstrated the accuracy, robustness and reliability of these models. Accordingly.the obtained results suggested that the proposed descriptors could be useful to predict the toxicity of phenols and anilines towards Chlorella vulgaris.