Overview
- Offers a comprehensive review of the methodologies that are currently used in computational toxicology
- Illustrates practical applications of computational toxicology in regulatory science
- Introduces emerging methods in computational toxicology, such as deep learning
Part of the book series: Challenges and Advances in Computational Chemistry and Physics (COCH, volume 30)
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Table of contents (19 chapters)
Keywords
- 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
About this book
Editors and Affiliations
About the editor
Bibliographic Information
Book Title: Advances in Computational Toxicology
Book Subtitle: Methodologies and Applications in Regulatory Science
Editors: Huixiao Hong
Series Title: Challenges and Advances in Computational Chemistry and Physics
DOI: https://doi.org/10.1007/978-3-030-16443-0
Publisher: Springer Cham
eBook Packages: Chemistry and Materials Science, Chemistry and Material Science (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-16442-3Published: 03 June 2019
Softcover ISBN: 978-3-030-16445-4Published: 14 August 2020
eBook ISBN: 978-3-030-16443-0Published: 21 May 2019
Series ISSN: 2542-4491
Series E-ISSN: 2542-4483
Edition Number: 1
Number of Pages: XVII, 412
Topics: Computer Applications in Chemistry, Pharmacology/Toxicology, Computer Appl. in Life Sciences, Theoretical and Computational Chemistry, Computational Biology/Bioinformatics