An Artificial Intelligence Platform for Automated PFAS Subgroup Classification: A Discovery Tool for PFAS Screening

16 August 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Since structural analyses and toxicity assessments have not been able to keep up with the discovery of unknown per- and polyfluoroalkyl substances (PFAS), there is an urgent need for effective categorization and grouping of PFAS. In this study, we presented PFAS Atlas, an artificial intelligence-based platform containing a rule-based automatic classification system and a machine learning-based grouping model. Compared with previously developed classification software, the platform’s classification system follows the latest Organization for Economic Co-operation and Development (OECD) definition of PFAS and reduces the number of uncategorized PFAS. In addition, the platform incorporates deep unsupervised learning models to visualize the chemical space of PFAS by clustering similar structures and linking related classes. Through real-world use cases, we demonstrate that PFAS Atlas can rapidly screen for relationships between chemical structure and persistence, bioaccumulation, or toxicity data for PFAS. The platform can also guide the planning of the PFAS testing strategy by showing which PFAS classes urgently require further attention. Ultimately, the release of PFAS Atlas will benefit both the PFAS research and regulation communities.


per- and polyfluoroalkyl substances
machine learning
chemical classification
chemical space
toxicity assessment

Supplementary materials

Supplementary Information
The interactive versions of Figure 2 to Figure 5 and the abbreviations for PFAS secondary classes are provided in Supplementary Information (S.I.).


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