Evaluating ionic liquid toxicity with machine learning and structural similarity methods

Abstract

Ionic liquids (ILs) have garnered significant interest owing to their distinct physicochemical traits. Nonetheless, their extensive application is curtailed by ecotoxicity concerns. This study aimed to develop a quantitative structure-activity relationship (QSAR) model for predicting the toxicity of ILs in biological cells. Toxicity data of ILs on leukemia rat cell line IPC-81, Escherichia coli (E. coli), and acetylcholinesterase (AChE) were collected from open-source databases, and two integrated models, random forest (RF) and gradient boosted decision tree (GBDT), were used to train the data. The molecular structures of the ILs were represented by three different methods, namely molecular descriptor (MD), molecular fingerprint (MF), and molecular identifier (MI), respectively. The Tanimoto similarity coefficients indicate that MD has a stronger ability to recognize structural similarity. Statistical metrics of model performance showed that the two models (MD-RF and MD-GBDT) with MD as an input feature performed better in the three datasets. The application of the SHapley Additive exPlanations (SHAP) method explains the importance of different features. Specifically, reducing the carbon chain length and the number of fluorine atoms in the structure of ILs can effectively reduce their toxic effects on biological cells. This study employs machine learning to grasp better how the structure of ILs relates to inhibiting biotoxicity, offering insights for crafting safer, eco-friendly IL designs.