Machine learning-driven QSAR models for predicting the cytotoxicity of five common microplastics.

Journal: Toxicology
PMID: 39137828

Abstract

In the field of microplastics (MPs) toxicity prediction, machine learning (ML) computer simulation techniques are showing great potential. In this study, six ML algorithms were utilized to predict the toxicity of MPs on BEAS-2B cells based on quantitative structure-activity relationship (QSAR) models. Comparing the models of different algorithms, the extreme gradient boosting model showed the best fit and prediction performance (R = 0.9876, R = 0.9286). Additionally, Williams plot analysis showed that the six models developed were able to predict stably within their applicability domain, with few outliers. Finally, the three feature importance methods-Embedded Feature Importance (EFI), Recursive Feature Elimination (RFE), and SHapley Additive exPlanations (SHAP)-consistently identified particle size as the most critical feature affecting toxicity prediction. The proposed QSAR model can be utilized for preliminary environmental exposure assessments of MPs and to better understand the associated health risks.

Authors

  • Chengzhi Liu
    College of Safety Science and Engineering, Nanjing Tech University, Nanjing, Jiangsu 210009, China. Electronic address: 18005194475@163.com.
  • Cheng Zong
    The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering , Xiamen University , Xiamen 361005 , P. R. China.
  • Shuang Chen
    The Beijing Genomics Institute (BGI), Shenzhen 518083, China. chenss@connect.hku.hk.
  • Jiangliang Chu
    College of Safety Science and Engineering, Nanjing Tech University, Nanjing, Jiangsu 210009, China. Electronic address: 18734001211@163.com.
  • Yifan Yang
    College of Food Science, Sichuan Agricultural University, Ya'an 625014, China.
  • Yong Pan
    School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, China.
  • Beilei Yuan
    College of Safety Science and Engineering, Nanjing Tech University, Nanjing, Jiangsu 210009, China. Electronic address: yuanbeilei@163.com.
  • Huazhong Zhang
    Department of Emergency Medicine, The First Affiliated Hospital with Nanjing Medical University, Nanjing, Jiangsu 210029, China; Institute of Poisoning, Nanjing Medical University, Nanjing 211100, China. Electronic address: zhanghuazhong313@163.com.

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