A "quantitative structure-activity relationship" (QSAR) model is developed to predict the toxicity of ionic liquids (ILs) based on the effect on the acetylcholinesterase (AChE) enzyme. A data set of 243 ILs was compiled and randomly divided into training (183 ILs) and test (60 ILs) sets to enable both internal and external validations. To optimize the model performance, a breaking point analysis was performed to identify the most relevant molecular descriptors. The analysis revealed that a set of 11 COSMO-RS quantum chemical descriptors provided near-optimal predictive power, with additional descriptors offering minimal improvement. A multiple linear regression (MLR) model was developed by using these descriptors, incorporating both cationic and anionic molecular features. Internal validation using Leave-One-Out and Leave-Many-Out cross-validation (Q2LOO = 0.79, Q2LMO = 0.78) as well as Y-scrambling confirmed the robustness of the model. External validation on the test set yielded acceptable R2 = 0.75 and low RMSE = 0.35 values, indicating strong predictive performance. The developed model outperformed previous models, particularly by accounting for the influence of anion structures, which have been largely neglected in earlier works. The final MLR-QSAR model not only demonstrated statistical reliability but also provided mechanistic insights into the structural contributions of both ionic components to IL's toxicity. Predicted toxicity values (Log 1/EC50) for novel ILs are also presented, expanding our understanding of IL safety profiles.
Gorji et al. (Mon,) studied this question.
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