Machine learning frameworks for predicting pulmonary cell toxicities induced by metal ions in the atmosphere.

Journal: Environmental pollution (Barking, Essex : 1987)
Published Date: Jan 15, 2026

Abstract

Metals in PM2.5 are closely associated with cardiopulmonary disease endpoints, potentially attributable to ionic species-induced oxidative stress effects. Computational prediction of metal ion respiratory toxicity remains challenging due to complex valence-dependent interactions. In this study, we constructed a predictive framework for high-throughput prediction of multiple metal ions toxicity endpoints. This framework leverages a 650-data-points multidimensional experimental dataset capturing dual pulmonary toxicological endpoints in cellular models, characterized by 31 valence-specific features, to identify key toxicological drivers simultaneously. Robust machine learning models established here accurately predicted metal ion respiratory toxicity (R2=0.89 for cell viability, ACC=0.86 for oxidative stress), validated externally (R2=0.74, ACC=0.70) by conducting Experimental validation on 10 new metal ions independent of the modeling dataset. Machine learning-driven feature selection ranked solubility, electronegativity, and cation charge of metal ions as top cell viability predictors, while the Pearson softness coefficient, molar mass, and density as vital parameters of oxidative stress. The predictive framework establishes property-toxicity relationships for PM2.5 metal ions, providing an effective tool for air pollution risk prioritization and mechanistic insights, thereby reducing unnecessary experimental burden.

Authors

  • Yang Huang
    School of Computer and Electronic Information, Nanjing Normal University, Nanjing 210023, China.
  • Xiu Chen
    Department of Environmental Science and Engineering, Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai 200433, China.
  • Tianqin Wang
    School of Chemistry and Materials Science, Ludong University, Yantai, 264025, China.
  • Di Wu
    University of Melbourne, Melbourne, VIC 3010 Australia.
  • Jiajun Ma
    Department of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology, 999077, Hong Kong China.
  • Hongwu Zhang
    School of Chemistry and Materials Science, Ludong University, Yantai, 264025, China.
  • Xuehua Li
    Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Linggong Road 2, Dalian 116024, China.
  • Qing Li
    Department of Internal Medicine, University of Michigan Ann Arbor, MI 48109, USA.

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