Advances in Computational Toxicology Methodologies and Applications in Regulatory Science

Advances in Computational Toxicology Methodologies and Applications in Regulatory Science / edited by Huixiao Hong.

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...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Hong, Huixiao (Editor)
Format: eBook
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2019.
Edition:1st ed. 2019.
Series:Challenges and Advances in Computational Chemistry and Physics, 30
Springer eBook Collection.
Subjects:
Chemoinformatics.
Pharmacology.
Bioinformatics .
Computational biology .
Chemistry, Physical and theoretical.
Bioinformatics.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • Computational Toxicology Promotes Regulatory Science
  • Tasks, Major Challenges and Emerging Modelling Methods for Computational Toxicology
  • Xenobiotic Metabolism by Cytochrome P450s: Insights Gained from Molecular Simulations
  • Applications of Molecular Modeling to Probe the Mechanism of Endocrine Disruptor Action
  • Mixture Toxicity
  • Towards reproducible in silico practice via OpenTox
  • Combining Machine Learning and Multilayer Networks for Toxicity Prediction
  • Matrix and tensor factorization for toxicity modelling
  • Network-based In Silico Assessment of Drug Cardiotoxicity
  • Mode-of-action-guided chemical toxicity prediction: A novel in silico approach for predictive toxicology
  • Machine learning methods for toxicity analysis
  • Predictive modeling of Tox21 data
  • The NTP DrugMatrix Toxicogenomics Database and Analysis Tool
  • Applications of Computational Toxicology for Risk Assessment of Food Ingredients and Indirect Food Additives
  • In silico prediction of the point of departure (POD) with high throughput data
  • The application of topic modeling on drug safety signal detection and analysis
  • Molecular dynamics simulations and applications in computational toxicology
  • Computational modeling for prediction of drug-induced liver injury in humans
  • Genomics in vitro to in vivo extrapolation (GIVIVE) for drug safety evaluation.

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