title: Use of artificial neural networks in the QSAR prediction of physicochemical properties and toxicities for REACH legislation.
abstract: With the introduction of the REACH legislation in the European Union, there is a requirement for property and toxicity data on chemicals produced in or imported into the EU at levels of 1 tonne/year or more. This has meant an increase in the in silico prediction of such data. One of the chief predictive approaches is QSAR (quantitative structure-activity relationships), which is widely used in many fields. A QSAR approach that is increasingly being used is that of artificial neural networks (ANNs), and this chapter discusses its application to the range of physicochemical properties and toxicities required by REACH. ANNs generally outperform the main QSAR approach of multiple linear regression (MLR), although other approaches such as support vector machines sometimes outperform ANNs. Most ANN QSARs reported to date comply with only two of the five OECD Guidelines for the Validation of (Q)SARs.
authors.affiliation: School of Pharmacy & Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK, J.C.Dearden@ljmu.ac.uk.
authors.forename: John C
authors.initials: JC
authors.lastname: Dearden
authors.forename: Philip H
authors.initials: PH
authors.lastname: Rowe
mesh_terms: D000818:Animals; D058735:Environmental Policy / Q000331:legislation & jurisprudence*; D004785:Environmental Pollutants / Q000633:toxicity*; D005062:European Union; D006801:Humans; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship
chemical_list: D004785:Environmental Pollutants