Toxicity assessment and i-QSTTR analysis of ionic liquids on D. magna, D. rerio, and R. subcapitata

The study aimed to assess the impacts of ionic liquids (ILs) as innovative alternatives to traditional organic solvents on aquatic environments and human health. Five machine learning methods, including multiple linear regression (MLR), partial least squares regression (PLS), random forest regression (RF), support vector regression (SVR), and extreme gradient boosting (XGBoost), were used to construct the prediction models of the toxicity of ILs to D. magna, D. rerio, and R. subcapitata. Rigorous validation criteria were implemented to evaluate the robustness and predictive accuracy of these models. The results indicated SVR and XGBoost models demonstrated superior predictive performance. In addition, for these three species of D. magna, D. rerio, and R. subcapitata. The six interspecies quantitative structure-toxicity-toxicity (i-QSTTR) models were developed to analyze the cross-species toxicity responses of ILs. The results revealed a strong interspecies correlation in the toxicity of ILs to D. magna and D. rerio, as well as between D. rerio and R. subcapitata. However, the correlation between D. magna and R. subcapitata was weaker, indicating significant differences in the responses of ILs toxicity between these two aquatic species. This study not only filled the data gap in the biotoxicity of ILs but also provided an important theoretical basis for their safe application.