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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…mehr
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 thisfield.
Dr. Huixiao Hong is the Chief of Bioinformatics Branch at the Division of Bioinformatics and Biostatistics, National Center for Toxicological Research (NCTR), FDA, Arkansas, USA and an Adjunct Professor at the University of Arkansas at Little Rock. He received his Ph.D. in computational chemistry from Nanjing University. Before he joined NCTR in 2007, Dr. Hong worked as the Manager of Bioinformatics Division at ICF International, as a senior computational scientist in ROW Science and held a research scientist position at Sumitomo Chemical Company, Japan. He was also a visiting scientist at National Cancer Institute at National Institutes of Health (NIH), USA and at Maxwell Institute in Leeds University, UK. Dr. Hong was an Associated Professor at Nanjing University in China. His research interests span the areas of chemoinformatics, computational chemistry, next-generation sequencing data analysis, genome-wide association studies, proteomics, and systems biology. Dr. Huixiao Hong has published more than 180 scientific manuscripts and received many awards during his career.
Inhaltsangabe
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.
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.
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.
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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