Insight into the Structural Basis of Environmental Compounds Activating the Aryl Hydrocarbon Receptor: A Hydrophobic Pharmacophore Revealed by Computational Modeling

The aryl hydrocarbon receptor (AhR) serves as a pivotal mediator in the pathogenesis of liver injury triggered by diverse environmental contaminants. To systematically identify novel AhR activators and elucidate their structural basis, this study implemented an integrated strategy spanning from machine learning-based prediction to mechanistic validation. The robust quantitative structure-activity relationship (QSAR) modeling identified the Random Forest algorithm combined with Morgan fingerprints as the optimal predictor of AhR activation. Structure–activity relationship analysis further revealed a conserved N-benzoylaniline scaffold as essential for bioactivity. Deeper computational interrogation through SHapley Additive exPlanations (SHAP) analysis revealed that a synergistic chloroimidazole-phenylamine signature is critical for AhR recognition. Furthermore, Bayesian Kernel Machine Regression (BKMR) quantified molecular hydrophobicity as the predominant physicochemical driver. Mechanistically, this hydrophobicity facilitates ligand entry into a specific hydrophobic pocket within the PAS-B domain in AhR, thereby enabling a crucial interaction between residues D327 and R396 that is responsible for receptor activation. This study ultimately delivers a comprehensive computational framework for predicting AhR activating compounds, which might provide a guidance for hepatotoxic risk.