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Quantitative structure-activity relationship models for genotoxicity prediction based on combination evaluation
Xiaotong Yang1, Zhengbao Zhang2, Qing Li3
1School of Public Health, Guangdong Pharmaceutical University, Guangzhou, China.
Scientific Reports
|April 14, 2021
Summary
This study developed advanced Quantitative Structure-Activity Relationship (QSAR) models for predicting compound mutagenicity. These fusion models integrate diverse experimental data, improving early warning systems for chemical toxicity.
Area of Science:
- Toxicology
- Computational Chemistry
- Bioinformatics
Background:
- Mutagenicity poses significant human health risks.
- Conventional toxicity prediction methods are limited in scope and predictive power.
- Existing models often lack integrated experimental data, hindering accuracy.
Purpose of the Study:
- To develop robust mutagenicity prediction models by integrating diverse experimental data.
- To enhance the predictive ability and application scope of computational toxicology tools.
- To establish an effective early warning system for compound mutagenicity.
Main Methods:
- Data from GENE-TOX, CPDB, and CCIS were partitioned using the weight-of-evidence method.
- Inclusion of in vivo, in vitro, prokaryotic, and eukaryotic experimental data following ICH guidelines.
- Development of nine sub-models and three fusion models using Random Forest (RF), Support Vector Machine (SVM), and Back Propagation (BP) algorithms.
Main Results:
- Fusion models demonstrated excellent predictive performance across all algorithms.
- The RF, SVM, and BP fusion models achieved prediction accuracies of 83.4%, 80.5%, and 79.0%, respectively.
- Area Under the Curve (AUC) values reached 0.853 (RF), 0.897 (SVM), and 0.865 (BP).
Conclusions:
- Integrated QSAR models effectively predict compound mutagenicity.
- The developed fusion models offer a reliable early warning system for potential mutagens.
- This approach enhances the assessment of chemical safety and reduces reliance on single experimental types.
