Application of interpretable machine learning models to improve the prediction performance of ionic liquids toxicity

Abstract

With the wide application prospect of ionic liquids (ILs) as solvent in the future industry, in order to promote green and sustainable chemical engineering, the toxicity problem of common concern has been systematically modeled. Machine learning has promoted the development of chemical property prediction model with its powerful data processing ability. Two typical ensemble learning models, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), were used to model the toxicity of ILs to Vibrio fischeri in this work. The model's hyperparameters were fine-tuned using Bayesian optimization, and its robustness was enhanced through the 5-fold cross validation. The results of the model comparison showed that the XGBoost model exhibited good generalization ability. In addition, the SHapley Additive exPlanations (SHAP) method was used to explain the model in more detail and the XGBoost model was used to supplement the toxicity value matrix of 1590 ILs.