Screening of estrogen receptor activity of per- and polyfluoroalkyl substances based on deep learning and in vivo assessment

Abstract

Over the past decades, exposure to per- and polyfluoroalkyl substances (PFAS), a group of synthetic chemicals notorious for their environmental persistence, has been shown to pose increased health risks. Despite that some PFAS were reported to have endocrine-disrupting toxicity in previous studies, accurate prediction models based on deep learning and the underlying structural characteristics related to the effect of molecular fluorination remain limited. To address these issues, we proposed a stacking deep learning architecture, GXDNet, that integrates molecular descriptors and molecular graphs to predict the estrogen receptor α (ERα) activities of compounds, enhancing the generalization ability compared to previous models. Subsequently, we screened the ERα activity of 10,067 PFAS molecules using the GXDNet model and identified potential ERα binders. The representative PFAS molecules with the top docking scores showed that the introduction of fluorinated alkane chains significantly increased the binding affinities of parent molecules with ERα, suggesting that the combination of phenol structural fragments and fluorinated alkane chains has a synergistic effect in improving the binding capacity of the ligands to ERα. The binding modes, SHapley Additive Explanations analysis, and attention map emphasized the importance of π-π stacking and hydrogen bonding interactions with the phenol group, while the fluorinated alkane chain enhanced the interaction with the hydrophobic amino acids of the active pocket. Experimental validation using zebrafish models further confirmed the ERα activity of the representative PFAS molecules. Overall, the current computational workflow is beneficial for the toxicological screening of emerging PFAS and accelerating the development of eco-friendly PFAS molecules, thereby mitigating the environmental and health risks associated with PFAS exposure.