Importance of Leveraging Machine Learning to Enhance Chemical Toxicity Assessment

The direct classification approach enhances in-silico chemical hazard and risk assessment by incorporating more hazard categories, such as chronic toxicity and environmental impact. Machine learning significantly improves chemical danger assessment by establishing relationships between molecular characteristics and biological activity, outperforming traditional QSAR models. Given the vast number of chemicals with limited toxicity data, modeling approaches help predict hazards efficiently. The direct classification method reduces incorrect categorization rates by grouping chemicals into predefined toxicity categories, improving accuracy and regulatory compliance. Emerging technologies like nanotechnology, advanced battery materials, and biotechnology drive innovation in the sector, while blockchain strengthens cybersecurity and supply chain integrity.