Machine Learning and Artificial Intelligence in Toxicology and Environmental Health

Machine Learning and Artificial Intelligence in Toxicology and Environmental Health introduces the fundamental concepts and principles of machine learning and AI, providing clear explanations on applying these methods to toxicology and environmental health. The book delves into predictions of chemical ADMET properties, development of PBPK and QSAR models, toxicogenomic analysis, and the evaluation of high-throughput in vitro assays. It aims to guide readers in adapting machine learning and AI techniques to various research problems within these fields. Additionally, the text explores ecotoxicology assessment, impacts of air pollution, climate change, food safety, and chemical risk assessment.

It includes case studies, hands-on computer exercises, and example codes, making it a comprehensive resource for researchers, academics, students, and industry professionals. The book highlights how AI can enhance risk assessment, predict environmental hazards, and speed up the identification of harmful substances.

• Covers the basic concepts and principles of commonly used machine learning and AI methods in the field of toxicology and environmental health
• Provides an introduction to the applications of machine learning and AI methods in toxicology and environmental health
• Offers case studies, example codes, and hands-on computer exercises to help readers apply machine learning and artificial intelligence (AI) methods in toxicology and environmental health