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 as well as provide explanations on how to apply machine learning and AI methods to the study toxicology and environmental health. The book explores topics such as predictions of chemical absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, the development of physiologically based pharmacokinetic (PBPK) and quantitative structure-activity relationship (QSAR) models, toxicogenomic analysis, analysis of high-throughput in vitro assays, ecotoxicology assessment, air pollution, climate change, food safety and chemical risk assessment. It also punctuates these topics with case studies, hands-on computer exercises, examples codes. Machine Learning and Artificial Intelligence in Toxicology and Environmental Health is a comprehensive and accessible source that provides researchers, academics, advanced undergraduate and graduate students, and industry professionals with guidance on how to adapt machine learning and AI methods to address specific research problems in toxicology and environmental health and also enables them to learn more about how AI can enhance risk assessment, predict environmental hazards, and expedite the identification of potentially harmful substances.