Unveiling the Structural Determinants of PFAS Toxicity: A Graph Neural Network and Interpretability Analysis
An Su1,2, Yushuang Zhai2, Chengwei Zhang2
1Zhejiang Key Laboratory of Green Manufacturing Technology for Chemical Drugs, College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou310014, China.
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The rapid proliferation of per- and polyfluoroalkyl substances (PFASs) necessitates high-throughput tools to assess safety and prevent regrettable substitution. However, conventional toxicological testing is resource-intensive, while standard computational methods often lack the mechanistic transparency required for rational molecular design. This study presents a deep learning framework coupling graph neural networks (GNNs) with transfer learning to predict acute oral rat LD50 toxicity in data-scarce environments. Among the architectures evaluated, GIN with stratified transfer learning (GIN-TL-Strat) achieved the highest predictive correlation (test R2 = 0.7426). By leveraging knowledge transferred from a broad chemical space, the model demonstrates promising generalization potential to structurally diverse fluorinated compounds. GNNExplainer was integrated as an interpretability mechanism to map prediction logic back to specific atomic substructures, enabling quantitative disentanglement of the contributions of chain length, functional group composition, and structural isomerism to acute toxicity. The results demonstrate that the fluorinated carbon chain length is the primary structural determinant of toxicity in continuous-chain alkyl PFAS. Crucially, attribution magnitude does not linearly dictate toxicity impact: while fluorinated backbones and specific nitrogenous/polar functionalities drive positive toxicity shifts, heavy halogens (I, Br, and Cl) exhibit the lowest mean attribution scores and correlate with reduced predicted toxicity, highlighting a decoupling between model attention and net toxicological outcomes. Structural isomerism between straight-chain and branched PFAS was found to have no statistically meaningful effect on predicted toxicity. This work transitions computational toxicology from a passive screening tool to an active platform for the safety-by-design of next-generation fluorinated alternatives.