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Machine Learning Models for High-Throughput Screening of Obesogens Based on Pathway Network.

Xiaoqing Wang1, Yang Huang2, Fei Li1

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Summary
This summary is machine-generated.

Scientists developed a machine learning system using adverse outcome pathway networks (AONs) to identify metabolism-disrupting chemicals linked to obesity. This screening tool accurately predicts obesogens and guides the development of safer chemicals.

Keywords:
Adverse outcome pathway networks (AONs)Computational toxicologyMachine learningMetabolic syndromeObesity

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Area of Science:

  • Environmental Toxicology
  • Computational Chemistry
  • Metabolic Disease Research

Background:

  • Metabolism-disrupting chemicals are linked to rising obesity rates via complex signaling pathways.
  • Systematic profiling of obesogenic factors and their mechanisms of action is crucial for public health.
  • Adverse Outcome Pathway (AOP) networks provide a framework for understanding chemical-induced toxicity.

Purpose of the Study:

  • To establish a machine learning screening system integrating AOPs to identify and profile chemical-induced obesity mechanisms.
  • To prioritize potential obesogenic chemicals and predict their modes of action.
  • To develop a high-throughput strategy for evaluating emerging chemicals' obesogenic potential.

Main Methods:

  • Integrated three AOPs into a machine learning screening system for thousands of chemicals.
  • Utilized AON-informed models to predict obesity-related chemical structures and mechanisms.
  • Selected top-prioritized chemicals for experimental verification of predicted pathway interference.

Main Results:

  • Machine learning models achieved >0.95 accuracy, identifying amide, aromatic/polycyclic, and nitrogen-containing structures as obesity-related.
  • Polycyclic aromatic hydrocarbons were identified as potent obesogens affecting multiple signaling pathways.
  • Predicted obesogens like antibiotics and insecticide metabolites targeted specific signaling pathways (membrane, mitochondrial, nuclear receptors).
  • Experimental verification confirmed pathway interference for prioritized chemicals, validating the model's predictive accuracy.

Conclusions:

  • The developed high-throughput screening strategy efficiently identifies potential obesogenic effects of chemicals.
  • The system provides valuable guidance for the safe design of new chemicals, mitigating obesity risks.
  • This AOP-informed approach advances the understanding and prediction of chemical-induced metabolic disruption.