Predictive Toxicology

Christoph Helma, University of Freiburg, Germany

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.