Development of quantitative interspecies toxicity relationship modeling of chemicals to fish

In this work, quantitative interspecies-toxicity relationship methodologies were used to improve the prediction power of interspecies toxicity model. The most relevant descriptors selected by stepwise multiple linear regressions and toxicity of chemical to Daphnia magna were used to predict the toxicities of chemicals to fish. Modeling methods that were used for developing linear and nonlinear models were multiple linear regression (MLR), random forest (RF), artificial neural network (ANN) and support vector machine (SVM). The obtained results indicate the superiority of SVM model over other models. Robustness and reliability of the constructed SVM model were evaluated by using the leave-one-out cross-validation method (Q(2)=0.69, SPRESS=0.822) and Y-randomization test (R(2)=0.268 for 30 trail). Furthermore, the chemical applicability domains of these models were determined via leverage approach. The developed SVM model was used for the prediction of toxicity of 46 compounds that their experimental toxicities to a fish were not being reported earlier from their toxicities to D. magna and relevant molecular descriptors.