Development, Evaluation and Application of a Multi-Representation Fusion Model for Accurate Prediction of Per- and Polyfluoroalkyl Substance (PFAS) Binding to Plasma Proteins

Per- and polyfluoroalkyl substances (PFAS) constitute a large and structurally diverse class of man-made chemicals. Their strong carbon-fluorine (C-F) bonds confer high environmental persistence, bioaccumulation, and various associated toxicities. As amphiphilic compounds, most PFAS bind to proteins and accumulate in protein-rich tissues, with such bioaccumulation exerting significant adverse impacts on human health. Accurate evaluation of the binding status between PFAS and proteins constitutes an essential step in health risk assessment. Traditional experiments and certain modeling approaches for analyzing PFAS bioaccumulation suffer from drawbacks such as time-consuming processes, high costs, or inadequate capture of molecular structural information, while existing machine learning-based prediction methods rely on single molecular representation, making it difficult to comprehensively encode the structural information on PFAS. Here, we propose MURNet, a multirepresentation fusion network model integrating chemical descriptors, 2D molecular graphs, and molecular fingerprints to predict PFAS-plasma protein binding. Compared with the state-of-the-art baseline models, MURNet achieves the optimal comprehensive performance. The multirepresentation fusion strategy generates higher-quality molecular features. Tanimoto similarity applicability domain analysis demonstrates MURNet's capability to reliably predict PFAS homologues. Case studies reveal the effectiveness of MURNet in screening PFAS with potential binding affinity to human serum albumin (HSA).