Structural Classification of PFAS using Molecular Fingerprints and Graph Networks

This project provides a network-based structural classification resource for 13,028 per- and polyfluoroalkyl substances (PFAS) from the U.S. EPA’s 2024 PFAS8a7v3 list. Using eight molecular fingerprinting methods and K-Nearest Neighbor Graphs (K-NNGs), we assign Proximity Classes to 288 previously unclassified PFAS compounds. The dataset includes Proximity Class outputs, UMAP coordinates, edge lists, and interactive 3D network visualizations across multiple neighborhood sizes. All files are designed for reuse in regulatory prioritization, chemical substitution, and machine learning applications.