Prediction of ionic liquid toxicity by interpretable machine learning

Haijun Feng; Li Jiajia; Zhou Jian

doi:10.1016/j.cjche.2025.04.018

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Prediction of ionic liquid toxicity by interpretable machine learning

Full Length Article | Updated：2026-03-26

- Prediction of ionic liquid toxicity by interpretable machine learning
- Prediction of ionic liquid toxicity by interpretable machine learning
- 中国化学工程学报（英文版） 2025年84卷第8期页码：201-210
- Affiliations：
  
  1. School of Computer and Software, Shenzhen Institute of Information Technology,Shenzhen,China,518172
  2. School of Chemistry and Chemical Engineering, Guangdong Provincial Key Laboratory for Green Chemical Product Technology, South China University of Technology,Guangzhou,China,510640
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2025.04.018
  中图分类号：
- 纸质出版：2025
- Accepted：
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Haijun Feng, Li Jiajia, Zhou Jian. Prediction of ionic liquid toxicity by interpretable machine learning[J]. 中国化学工程学报（英文版）, 2025,84(8):201-210.

Haijun Feng, Li Jiajia, Zhou Jian. Prediction of ionic liquid toxicity by interpretable machine learning[J]. Chinese Journal of Chemical Engineering, 2025, 84(8): 201-210.
Haijun Feng, Li Jiajia, Zhou Jian. Prediction of ionic liquid toxicity by interpretable machine learning[J]. 中国化学工程学报（英文版）, 2025,84(8):201-210. DOI： 10.1016/j.cjche.2025.04.018.

Haijun Feng, Li Jiajia, Zhou Jian. Prediction of ionic liquid toxicity by interpretable machine learning[J]. Chinese Journal of Chemical Engineering, 2025, 84(8): 201-210. DOI： 10.1016/j.cjche.2025.04.018.

摘要

Abstract

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 (

). 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.

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