Predicting Bioconcentration Factors of Per- and Polyfluoroalkyl Substances Using a Directed Message Passing Neural Network with Multimodal Feature Fusion.

Journal: Environmental science & technology
Published Date: May 20, 2025

Abstract

Amid growing concerns regarding the ecological risks posed by emerging contaminants, per- and polyfluoroalkyl substances (PFASs) present significant challenges for risk assessment due to their structural diversity and the paucity of experimental data on their bioaccumulation. This study investigated the bioconcentration factors (BCFs) of 18 emerging and legacy PFASs using zebrafish in a flow-through exposure system and constructed a robust BCF prediction model to address the data gaps associated with numerous novel PFASs. Experimental results indicated that perfluoro(3,5,7,9,11-pentaoxadodecanoic) acid (PFO5DoDA) and perfluoro-2,5-dimethyl-3,6-dioxanonanoic acid (C9 HFPO-TA) exhibited higher bioaccumulation potential than perfluorooctanoic acid (PFOA). A multimodal feature-fused directed message passing neural network (FF-DMPNN) model was constructed, integrating molecular graph representations, physicochemical descriptors, and bioassay data reflecting absorption, distribution, metabolism, and excretion characteristics. The FF-DMPNN model demonstrated superior predictive performance compared to conventional machine learning approaches by providing a more complete representation of molecular structures and physicochemical properties, achieving higher accuracy (R = 0.742) and robustness in predicting BCF values for PFASs. Application of the model to a comprehensive PFAS database identified 2.45% of chemicals as bioaccumulative, highlighting the need for regulatory attention. Overall, this study provides critical insights into the bioconcentration risks associated with PFASs and offers a reliable framework for prioritizing regulatory actions for these emerging contaminants, addressing a pressing need for their effective environmental management.

Authors

  • Jingzhi Yao
    Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China.
  • Yingqing Shou
    Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China.
  • Nan Sheng
    School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China.
  • Yu Ma
    Department of Electronic Engineering, Fudan University, Shanghai, China.
  • Yitao Pan
    State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
  • Feng Zhao
    Department of Blood Transfusion, The First Affiliated Hospital of Ningbo University, Ningbo, China.
  • Mingliang Fang
    School of Civil and Environmental Engineering, Nanyang Technological University , 639798 Singapore.
  • Jiayin Dai
    State Key Laboratory of Green Papermaking and Resource Recycling, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

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