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Related Experiment Video

Updated: Jul 2, 2025

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow
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An artificial intelligence platform for automated PFAS subgroup classification: A discovery tool for PFAS screening.

An Su1, Yingying Cheng1, Chengwei Zhang2

  • 1State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, Key Laboratory of Green Chemistry-Synthesis Technology of Zhejiang Province, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China; Key Laboratory of Pharmaceutical Engineering of Zhejiang Province, Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, PR China.

The Science of the Total Environment
|February 25, 2024
PubMed
Summary

A new AI platform, PFAS-Atlas, categorizes and groups per- and polyfluoroalkyl substances (PFAS) more effectively. It aids researchers and regulators by visualizing PFAS chemical space and guiding testing strategies.

Keywords:
BioaccumulationChemical classificationChemical spaceMachine learningPFASPer- and polyfluoroalkyl substancesToxicity assessment

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Area of Science:

  • Environmental Chemistry
  • Computational Chemistry
  • Toxicology

Background:

  • Rapid discovery of per- and polyfluoroalkyl substances (PFAS) outpaces structural analysis and toxicity assessment.
  • Effective categorization and grouping of PFAS are urgently needed for research and regulation.

Purpose of the Study:

  • To introduce PFAS-Atlas, an AI-based platform for classifying and grouping PFAS.
  • To improve upon existing software by adhering to the latest OECD definition of PFAS and minimizing uncategorized substances.
  • To visualize the chemical space of PFAS and identify relationships between chemical structure and potential risks.

Main Methods:

  • Development of a rule-based automatic classification system for PFAS.
  • Implementation of a machine learning-based grouping model using deep unsupervised learning.
  • Clustering of similar PFAS structures and linking of related chemical classes.
  • Application of the platform to real-world use cases for screening and strategy planning.

Main Results:

  • PFAS-Atlas successfully classifies PFAS according to the latest OECD definition, reducing uncategorized compounds.
  • Deep unsupervised learning visualizes the PFAS chemical space, revealing clusters of similar structures.
  • The platform demonstrates rapid screening of structure-activity relationships for persistence, bioaccumulation, and toxicity.
  • PFAS-Atlas effectively guides PFAS testing strategies by highlighting areas needing further investigation.

Conclusions:

  • PFAS-Atlas provides a powerful tool for the classification and grouping of PFAS.
  • The platform enhances understanding of PFAS chemical space and structure-activity relationships.
  • PFAS-Atlas will significantly benefit the PFAS research and regulatory communities by improving efficiency and informing testing strategies.