The potential toxicity of ionic liquids (ILs) affects their applications; how to control the toxicity is one of the key issues in their applications. To understand its toxicity structure relationship and promote its greener application

six different machine learning algorithms

including Bagging

Adaptive Boosting (AdaBoost)

Gradient Boosting (GBoost)

Stacking

Voting and Categorical Boosting (CatBoost)

are established to model the toxicity of ILs on four distinct datasets including Leukemia rat cell line IPC-81 (IPC-81)

Acetylcholinesterase (AChE)

Escherichia coli

(

E.coli

) and

Vibrio fischeri

. Molecular descriptors obtained from the simplified molecular input line entry system (SMILES) are used to characterize ILs. All models are assessed by the mean square error (MSE)

root mean square error (RMSE)

mean absolute error (MAE) and correlation coefficient (

R

2

). Additionally

an interpretation model based on SHapley Additive exPlanations (SHAP) is built to determine the positive and negative effects of each molecular feature on toxicity. With additional parameters and complexity

the Catboost model outperforms the other models

making it a more reliable model for ILs' toxicity prediction. The results of th

e model's interpretation indicate that the most significant positive features

SMR_VSA5

PEOE_VSA8

Kappa2

PEOE_VSA6

SMR_VSA5

PEOE_VSA6 and EState_VSA1

can increase the toxicity of ILs as their levels rise

while the most significant negative features

VSA_EState7

EState_VSA8

PEOE_VSA9 and FpDensityMorgan1

can decrease the toxicity as their levels rise. Also

an IL's toxicity will grow as its average molecular weight and number of pyridine rings increase

whereas its toxicity will decrease as its hydrogen bond acceptors increase. This finding offers a theoretical foundation for rapid screening and synthesis of environmentally-benign ILs.