title: Artificial neural network modeling in environmental toxicology.
abstract: Artificial neural networks are increasingly used in environmental toxicology to find complex relationships between the ecotoxicity of xenobiotics and their structure and/or physicochemical properties. The raison d'etre of these nonlinear tools is their ability to derive powerful QSARs for molecules presenting different mechanisms of action. In this chapter, the main QSAR models derived for aquatic and terrestrial species are reviewed. Their characteristics and modeling performances are deeply analyzed.
authors.affiliation: CTIS, Rillieux La Pape, France.
authors.forename: James
authors.initials: J
authors.lastname: Devillers
mesh_terms: D000818:Animals; D001419:Bacteria / Q000378:metabolism; D002627:Chemistry, Physical / Q000379:methods; D003445:Crustacea; D004463:Ecology / Q000379:methods; D017753:Ecosystem; D054750:Ecotoxicology / Q000379:methods*; D004784:Environmental Monitoring / Q000379:methods; D056890:Eukaryota / Q000378:metabolism; D005399:Fishes; D007313:Insecta; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship; D015262:Xenobiotics / Q000737:chemistry
chemical_list: D015262:Xenobiotics