520 pages | 36 Color Illus. | 74 B/W Illus.
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.
“… This reference book 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 non-commercial predictive toxicology programs, and advanced technologies in computational chemistry and biology, statistics, and data mining. … Valuable to readers in a variety of disciplines, such as toxicologists, pharmacologists, computer scientists, statisticians, and researchers in environmental toxicology and drug design, this guide provides demonstrations of various algorithms and their capabilities to select, calculate, and represent the features and properties of chemical structures, demonstrations that og beyond the classical structure-activity relationships.”
— J Albaigés, Editor in Chief, Department of Environment Chemistry CID-CSIC, Barcelona, Spain, in International Journal Of Environmental Analytical Chemistry, Vol. 86, 2006
“…provides in-depth reviews of subjects widely considered by workers in this developing field.”
“…After using the book, practitioners are likely to have gained a stronger sense of the methodologies behind the techniques….a valuable resource for a field that is quickly developing in practicality.”
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.