Machine learning models on chemical inhibitors of mitochondrial electron transport chain.

Journal: Journal of hazardous materials
PMID: 34920224

Abstract

Chemicals can induce adverse effects in humans by inhibiting mitochondrial electron transport chain (ETC) such as disrupting mitochondrial membrane potential, enhancing oxidative stress and causing some diseases. Thus, identifying ETC inhibitors (ETCi) is important to chemical risk assessment and protecting the public health. However, it is not feasible to identify all ETCi with experimental methods. Quantitative structure-activity relationship (QSAR) modeling is a promising method to rapidly and effectively identify ETCi. In this study, QSAR models for predicting ETCi were developed using machine learning methods. A clustering-based under-sampling (CBUS) method was developed to handle the imbalance issue in training sets. Structure-activity landscapes were generated and analyzed for training sets generated by the CBUS method. The consensus QSAR models constructed with CBUS achieved satisfactory performances (balanced accuracy = 0.852) in 100 iterations of five-fold cross validations, indicating the models can effectively classify ETCi. The classification model was further employed to screen chemicals in the Inventory of Existing Chemical Substances of China and 13 chemicals were identified as ETCi. Fifteen structural alerts for ETCi were identified in this study. These results demonstrated that the model and structural alerts are useful to screen ETCi.

Authors

  • Weihao Tang
    Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China.
  • Wenjia Liu
    2 Mechanical Engineering, University of California, Los Angeles (UCLA), Los Angeles, CA, USA.
  • Zhongyu Wang
    a Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology , Dalian University of Technology , Dalian , China.
  • Huixiao Hong
    National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Rd, Jefferson, AR, 72079, USA. Electronic address: Huixiao.Hong@fda.hhs.gov.
  • Jingwen Chen
    Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China. Electronic address: jwchen@dlut.edu.cn.

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