Integrating computational methods to predict mutagenicity of aromatic azo compounds

Area of Science:

• Environmental chemistry
• Toxicology
• Computational chemistry

Background:

• Azo dyes are widely used industrially but pose risks due to mutagenicity.
• Their degradation products can also exhibit harmful effects on ecosystems and human health.
• Predicting azo dye toxicity is crucial for risk assessment and mitigation.

Purpose of the Study:

• To develop and evaluate computational strategies for predicting azo dye mutagenicity.
• To compare the performance of knowledge-based methods and docking simulations.
• To integrate various in silico models into consensus strategies for improved accuracy.

Main Methods:

• Utilized a benchmark dataset of Ames test data for 354 azo dyes.
• Developed three classification strategies incorporating knowledge-based approaches and docking simulations.
• Compared and integrated results with existing literature models to form consensus strategies.

Main Results:

• The developed computational methods demonstrated significant accuracy in predicting azo dye mutagenicity.
• Consensus strategies, integrating multiple in silico models, showed improved predictive performance.
• The study confirmed the utility of computational toxicology for assessing azo compounds.

Conclusions:

• In silico methods provide a valuable, cost-effective tool for predicting azo dye mutagenicity.
• Computational approaches can support and complement experimental toxicity testing.
• These methods aid in the early identification of potentially hazardous azo compounds.