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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.
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
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.
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