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Mutagenicity and Carcinogenicity01:25

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Humans continually engage with an environment rich in potentially harmful chemicals. These are introduced to our bodies through inhalation, ingestion, or skin contact. These chemicals exist in various forms, such as air and environmental pollutants, agricultural chemicals, organic solvents, and heavy metals.
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Related Experiment Video

Updated: Jan 18, 2026

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods
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Toward Explainable Carcinogenicity Prediction: An Integrated Cheminformatics Approach and Consensus Framework for

Huynh Anh Duy1,2, Tarapong Srisongkram3

  • 1Graduate School in the Program of Research and Development in Pharmaceuticals, Faculty of Pharmaceutical Sciences, Khon Kaen University, Khon Kaen 40002, Thailand.

Journal of Chemical Information and Modeling
|September 12, 2025
PubMed
Summary

A consensus machine learning framework accurately predicts chemical carcinogenicity (IARC class 2B). This tool aids in identifying potential carcinogens, improving chemical safety assessments.

Area of Science:

  • Computational toxicology
  • Cheminformatics
  • Machine learning in drug discovery

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Background:

  • Carcinogenicity assessment is crucial for public health and regulatory decisions.
  • Classifying chemicals, especially those in the International Agency for Research on Cancer (IARC) class 2B (possibly carcinogenic), presents significant challenges.
  • Existing methods may lack the accuracy and efficiency needed for large-scale chemical screening.