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A database framework for rapid screening of structure-function relationships in PFAS chemistry.

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  • 1Department of Materials Design and Innovation, University at Buffalo, Buffalo, NY, USA.

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|January 19, 2021
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This study introduces PFAS-Map, a novel database framework for exploring structure-function relationships in per- and polyfluoroalkyl substances (PFAS). It aids in classifying and predicting properties of new PFAS chemistries.

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Area of Science:

  • Environmental Chemistry
  • Computational Chemistry
  • Data Science

Background:

  • Per- and polyfluoroalkyl substances (PFAS) pose environmental and health challenges.
  • Understanding PFAS structure-function relationships is crucial for risk assessment and regulation.
  • Existing methods for classifying and analyzing PFAS are limited in scope and speed.

Purpose of the Study:

  • To develop a database framework for rapid exploration of structure-function relationships in PFAS.
  • To create a tool for classifying and predicting properties of new and emerging PFAS chemistries.
  • To facilitate the integration of diverse data sources for PFAS analysis.

Main Methods:

  • Development of a data framework mapping molecular structure (SMILES) to functionality (bioactivity, physicochemical properties).
  • Implementation of 'PFAS-Map', a 3D unsupervised visualization tool.
  • Application of the framework for property prediction and classification of PFAS.

Main Results:

  • The PFAS-Map effectively visualizes high-dimensional PFAS data.
  • The tool can automatically classify new PFAS chemistries based on established criteria.
  • Demonstrated utility in predicting unmeasured physical properties and uncovering hierarchical classification characteristics.

Conclusions:

  • The PFAS-Map framework offers a powerful approach for understanding PFAS systematics.
  • This tool facilitates rapid exploration and classification of diverse PFAS chemistries.
  • Enables data fusion from various sources for comprehensive PFAS analysis.