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Related Experiment Video

Updated: Jun 26, 2026

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods
05:34

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Published on: June 6, 2025

AI-Enabled Mapping of Structure-Hazard Relationships for Emerging Contaminants.

Shuo Zhang1, Jun Guo1, Yunkun Qian2

  • 1Department of Environmental Science & Engineering, Fudan University, Shanghai 200238, China.

Environment & Health (Washington, D.C.)
|June 25, 2026
PubMed
Summary

This study introduces a framework to assess emerging contaminant hazards by integrating literature, molecular structure, and toxicity data. It enables rapid screening and prioritization of chemicals for safer substitution and regulation.

Keywords:
Emerging contaminantsLarge language modelsMachine learningPathway activityRegistry harmonization

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Published on: April 18, 2019

Area of Science:

  • Environmental Chemistry
  • Computational Toxicology
  • cheminformatics

Background:

  • Emerging contaminant hazards are a growing concern, but data is fragmented across various sources.
  • Existing evidence is scattered across literature, chemical registries, inventories, and bioassays, hindering comprehensive assessment.
  • A unified approach is needed to integrate diverse data streams for effective hazard evaluation.

Purpose of the Study:

  • To develop an integrated and reproducible framework for assessing emerging contaminant hazards.
  • To align literature-derived hazard signals with molecular structure and regulatory dimensions using a persistence, bioaccumulation, mobility, and toxicity (PBMT) lens.
  • To enable rapid screening, targeted data acquisition, and informed chemical substitution and regulatory updating.

Main Methods:

  • Utilized a task-tuned large language model to extract 21,277 mentions from 9,557 publications.
  • Harmonized chemical candidates across CompTox, PubChem, and Wikipedia, identifying 1,081 unique chemicals.
  • Applied AutoGluon models trained on Tox21/ToxCast for predictive toxicology on list-naïve chemicals.

Main Results:

  • Identified abundant toxicity information but limited mobility data for emerging contaminants.
  • Structure profiling revealed recurring motifs associated with composite hazards (e.g., halogenation).
  • Generated end-point-level toxicity predictions, primarily at the protein-function level, aiding prioritization.

Conclusions:

  • The developed framework effectively integrates textual, structural, regulatory, and predictive data streams.
  • This approach facilitates auditable chemical prioritization by combining model scores with PBMT evidence density.
  • The framework supports efficient hazard screening, targeted data generation, and adaptive regulatory strategies for emerging contaminants.