In this work, quantitative interspecies-toxicity relationship methodologies were used to improvement of 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 prediction of the toxicities of chemicals to fish. Methods that were used for developing linear and nonlinear models are; 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 (Q2= 0.69, SPRESS=0.822) and Y-randomization test (R2=0.268 for 30 trails). Furthermore, the chemical applicability domains of these models were determined via leverage approach. The developed SVM model was used for prediction of the toxicities of 46 compounds that their experimental toxicities to fish were not being reported earlier from their toxicities to Daphnia magna and relevant molecular descriptors.
