Description:
This advanced course equips students with cutting-edge tools and techniques for high-throughput screening, machine learning, and artificial intelligence in toxicology. Learners will delve into predictive modeling for environmental and health risks, explore real-world case studies that showcase the impact of computational toxicology in regulatory decisions, and acquire professional skills in developing and validating computational tools. The course also prepares students to navigate complex regulatory environments and effectively communicate toxicological risks to non-experts.
Learning Outcomes:
By the end of this course, students will be able to:
Apply high-throughput screening (HTS) and virtual screening techniques to identify potential toxicants.
Implement machine learning algorithms such as Random Forest and Neural Networks to predict chemical toxicity.
Develop and validate computational tools for toxicology, ensuring compliance with regulatory standards.
Use AI-driven models to improve the accuracy of toxicological predictions.
Communicate toxicological risks effectively to both expert and non-expert audiences.