In silico profiling of bisphenol compounds for estrogenic effects via pharmacophore modeling, molecular docking, and...

Bisphenol compounds represent a class of widely used chemicals that have become increasingly recognized as ubiquitous environmental pollutants, they are urgently calling for robust toxicity assessment before large-scale application. Leveraging in silico virtual screening technologies, we developed a comprehensive computational framework combining estrogen receptor (ER) pharmacophore modeling with molecular docking and dynamic simulations to assess the estrogenic potential of bisphenol compounds systematically. Successfully, we established models achieving ≥ 80 % discriminatory power, with 16 out of 30 generated ones demonstrating excellent performance. The virtual screening of 427 bisphenol compounds determined 74 with estrogenic potential, among which 32 exhibited multi-target binding ability to ≥ 3 pharmacophore models. Molecular docking analysis revealed the detailed structural basis for bisphenol-ER interactions, with bisphenol M and trisphenol demonstrating the highest binding capacity and each interacting with 7 different pharmacophore models. The follow-up molecular dynamics simulations analysis further validated bisphenol M and trisphenol binding stability to ERs. In conclusion, we established a validated computational framework that successfully identified 74 bisphenol compounds with estrogenic potential, including bisphenol M and trisphenol as top candidates with multi-target binding capabilities across ERα, ERβ, and ERRγ. The established framework provides an effective tool for regulatory screening and proactive hazard identification of bisphenol alternatives.