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Realistic Membrane Modeling Using Complex Lipid Mixtures in Simulation Studies
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Mechanism-Based Multitarget Modeling for Pathway-Level Prediction of PI3K/Akt Signaling Perturbation Induced by

Jiawei Cheng1, Yuhe He2, Yawen Yuan1

  • 1School of Chemistry and Life Resources, Renmin University of China, Beijing 100872, China.

Environmental Science & Technology
|June 18, 2026
PubMed
Summary

This study introduces a new framework to understand how liquid crystal monomers (LCMs) cause toxicity by analyzing their effects on cell signaling pathways. The research identified key cellular targets and predicted potential toxic effects of LCMs.

Keywords:
display industryemerging contaminantsgene protein interaction networkgraph neural networkpredictive modeltranscriptomic

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Area of Science:

  • Environmental toxicology
  • Computational toxicology
  • Molecular biology

Background:

  • Liquid crystal monomers (LCMs) are emerging contaminants with poorly understood toxicity mechanisms.
  • System-level toxicity assessment requires understanding perturbations at the signaling network level.

Purpose of the Study:

  • To develop a pathway-centric multitarget framework for characterizing LCM toxicity.
  • To identify key signaling pathways and protein targets affected by LCMs.
  • To predict the toxicological effects of LCMs using computational methods.

Main Methods:

  • Pathway enrichment analysis (KEGG) to identify key signaling axes.
  • Development of a multitask deep learning model trained on ChEMBL IC50 data.
  • Application of the model to predict inhibitors for 1412 LCMs.
  • Structural analysis of LCMs and integration with Gene Ontology enrichment.
  • Transcriptomic analysis in human lung epithelial cells (A549).

Main Results:

  • The PI3K/Akt pathway was identified as a key mechanistic axis.
  • A minimal set of 19 proteins was constructed for pathway monitoring.
  • The deep learning model achieved high accuracy (>0.85) in predicting inhibitors.
  • EGFR/JAK1 and PIK3CA were predicted as major targets for LCMs.
  • Predicted pathway perturbations linked to cell cycle arrest, apoptosis, and impaired cell migration.
  • Transcriptomic data confirmed PI3K/Akt pathway disruption in A549 cells.

Conclusions:

  • The developed framework enables mechanism-informed toxicity assessment of LCMs.
  • The study provides a method for prioritizing chemicals based on predicted toxicity.
  • This approach is valuable for toxicity assessment with limited experimental data.