A Database Framework for Rapid Screening of Structure-Function Relationships in PFAS Chemistry

This paper describes a database framework that enables one to rapidly explore systematics in structure-function relationships associated with new and emerging PFAS chemistries. The data infrastructure maps high dimensional information associated with SMILES encoding of molecular structure with activity/property data. This ‘PFAS-Map’ serves as a 3-dimensional unsupervised visualization learning tool to automatically classify new PFAS chemistries into current well-established criteria for PFAS classification. We provide examples on how the PFAS-Map can be utilized, including the ability to predict and estimate yet unmeasured fundamental physical properties of PFAS chemistries, uncovering hierarchical characteristics in existing classification schemes and the fusion of data from diverse sources.