Predictive Toxicology

A comprehensive overview of techniques and systems currently utilized in predictive toxicology, this reference presents an in-depth survey of strategies to characterize chemical structures and biological systems?covering prediction methods and algorithms, sources of high-quality toxicity data, the most important commercial and noncommercial predictive toxicology programs, and advanced technologies in computational chemistry and biology, statistics, and data mining.

A Brief Introduction to Predictive Toxicology. Description and Representation of Chemicals. Computational Biology and Toxicogenomics. Toxicological Information for Use in Predictive Modeling: Quality, Sources, and Databases. The Use of Expert Systems for Toxicology Risk Prediction. Regression- and Projection-Based Approaches in Predictive Toxicology. Machine Learning and Data Mining. Neural Networks and Kernel Machines for Vector and Structured Data. Applications of Substructure-Based SAR in Toxicology. OncoLogic: A Mechanism-Based Expert System for Predicting the Carcinogenic Potential of Chemicals. META: An Expert System for the Prediction of Metabolic Transformations. MC4PC—An Artificial Intelligence Approach to the Discovery of Quantitative Structure-Toxic Activity Relationships. PASS: Prediction of Biological Activity Spectra for Substances. Lazar: Lazy Structure-Activity Relationships for Toxicity Prediction. Index.

Professional

Christoph Helma, University of Freiburg, Germany