Machine learning-driven QSAR models for the prediction of metabolic mechanisms and thyroid hormone-disrupting effects of emerging pollutants in the human body: a case study of bisphenol analogues

Abstract

Research on theoretical prediction methods for elucidating the reaction mechanisms and thyroid hormone-disrupting effects of emerging pollutants in the human body faces significant challenges. The application of in silico methods utilizing machine learning is increasingly recognized as an effective strategy to determine the mechanisms of reactions catalyzed by human cytochrome P450 enzymes (CYPs) and to predict the adverse effects of emerging pollutants. Bisphenol analogues (BPs) represent one of the most important endocrine disruptors. These compounds can cause serious effects on the ecological environment and human health. Herein, density functional theory (DFT) calculations was employed to investigate the reaction mechanisms of the O-addition and H-abstraction of 20 BPs by human CYPs. A machine-learning-integrated quantitative structure–activity relationship (ML-QSAR) framework was established to predict the energy barriers of three types of reactions. Moreover, binding affinities were calculated between BPs (including their metabolites) and TRβ-LBD to evaluate their thyroid hormone-disrupting effects. The binding affinity data of BPs to TRβ-LBD were used as a training set to develop a ML-QSAR model for efficient prediction of potentially hazardous BPs. A ML-QSAR model with an R² of 0.869 was established to predict their thyroid hormone-disrupting effects. The study enhances our understanding of CYP catalytic mechanisms involving BPs and provides a reference for the future design of environmentally friendly BPs. The predictive model developed for thyroid hormone-disrupting effects can aid in identifying the adverse effects of emerging BPs, supporting more effective management of toxic compounds.