Computational Toxicology and AI in Environmental Science

This track focuses on the integration of computational tools, artificial intelligence (AI), and machine learning approaches in environmental toxicology and chemistry. It covers predictive toxicology models, in-silico screening of chemicals, and data-driven risk assessment techniques. Researchers will explore quantitative structure–activity relationship (QSAR) models, molecular docking, and simulation-based approaches to predict chemical toxicity without extensive animal testing. The track also highlights the use of big data analytics and environmental databases to identify toxicity patterns and emerging risks. Emphasis is placed on improving accuracy, reducing experimental costs, and accelerating decision-making in environmental safety assessments through digital innovation.

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