A Simple Approach to the Toxicity Prediction of Anilines and Phenols Towards Aquatic Organisms

Chemicals pollution in the environment has attracted attention all over the world, and the toxicity prediction of chemical pollutants has become quite important. In this paper, we introduce a simple approach to predict the toxicity of some chemical components, in which the Tchebichef image moment (TM) method was employed to extract useful chemical information from the images of molecular structures to establish quantitative structure–activity relationship (QSAR) prediction models. The proposed approach was applied to predict the toxicity of anilines and phenols for the aquatic organisms of P. subcapitata and V. fischeri, in which the obtained TMs were defined as the independent variables, while the biological toxicity (pEC50) was regarded to be the dependent variable. Then, the predictive models were established by stepwise regression, respectively. The obtained squared correlation coefficients of leave-one-out cross-validation (Q2) for training sets and the predictive squared correlation coefficients (Rp2) for test sets of the two groups of data were higher than 0.79 and 0.75, respectively, which indicated that the obtained models possessed satisfactory accuracy and reliability. Compared with several reported methods, the proposed approach was more convenient and has a higher predictive capability. Our study provides another perspective in QSAR research.