Advances in Computational Toxicology

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.