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
Clinical Snapshot
PICO Framework
| P — Population | Computational models using 20 bisphenol analogues as test compounds |
| I — Intervention | Machine learning-integrated quantitative structure-activity relationship (ML-QSAR) modelling combined with density functional theory calculations |
| C — Comparator | Traditional computational chemistry approaches without machine learning integration |
| O — Outcomes | Energy barriers for cytochrome P450-catalysed reactions, binding affinities to thyroid hormone receptor β ligand-binding domain (TRβ-LBD), and thyroid hormone-disrupting potential |
Bottom Line
This computational study developed predictive models for bisphenol analogue toxicity using density functional theory and machine learning. The resulting model achieved strong predictive performance (R² = 0.869) for thyroid hormone-disrupting effects. While the approach shows promise for rapid hazard screening without animal testing, the study's impact is limited by the small dataset of 20 compounds and lack of external validation. The methodology could support regulatory decision-making and safer chemical design, but requires validation with larger, more diverse chemical datasets before widespread implementation. Australian regulators could potentially incorporate such tools into existing chemical assessment frameworks, though experimental validation would remain necessary for definitive safety conclusions.
Key Findings
P Value: Not reported
Effect Size: R² = 0.869
Primary Outcome: ML-QSAR model performance for predicting thyroid hormone-disrupting effects
Nnt Or Sensitivity: Model explains 86.9% of variance in thyroid hormone-disrupting potential
Confidence Interval: Not reported
Clinical Application
High feasibility as computational tool requiring only chemical structure input Relevant to ACCC product safety assessments, TGA chemical evaluation, and NHMRC environmental health guidelines for endocrine disruptors Regulatory agencies, chemical manufacturers, and environmental health researchers assessing bisphenol analogue safety
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.
References
- 1.Wang, Z., Zhang, Q., Wang, W., & Wang, Q. (2026). 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. Environmental Science: Processes & Impacts. https://doi.org/10.1039/d5em00891c
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