About this book
This book provides a comprehensive review of both traditional and cutting-edge methodologies that are currently used in computational toxicology and specifically features its application in regulatory decision making. The authors from various government agencies such as FDA, NCATS and NIEHS industry, and academic institutes share their real-world experience and discuss most current practices in computational toxicology and potential applications in regulatory science. Among the topics covered are molecular modeling and molecular dynamics simulations, machine learning methods for toxicity analysis, network-based approaches for the assessment of drug toxicity and toxicogenomic analyses. Offering a valuable reference guide to computational toxicology and potential applications in regulatory science, this book will appeal to chemists, toxicologists, drug discovery and development researchers as well as to regulatory scientists, government reviewers and graduate students interested in this field.
Computational Toxicology Regulatory Science Toxicology Machine Learning Toxicology In Silico Toxicology Deep Learning for Toxicity Prediction Deep Learning Toxicology Toxicity Prediction Toxicity Modeling Toxicity Analysis Toxicology Simulations Database Toxicology Toxicogenomics Endocrine Disruptors Mixture Toxicity Regulatory Science
Springer Nature Switzerland AG 2019
Chemistry and Materials Science
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