title: Neural modelling of the biodegradability of benzene derivatives.
abstract: The aim of this paper was to explore the usefulness of a backpropagation neural network (BNN) to estimate the biodegradability of benzene derivatives. 127 chemicals selected from the BIODEG data bank (Syracuse Research Corporation, 1992) were described by means of 20 structural descriptors taking into account the nature and position of the substituents on the benzene ring. Three classes of biodegradability were selected and modelled from the BNN. A 20/5/3 BNN (alpha = 0.8 and eta = 0.5) correctly classified 92% (104/113) of the training and 86% (12/14) of the testing sets. The results were compared to those produced by the BIODEG probability program (Syracuse Research Corporation, Version 2.13).
authors.affiliation: CTIS, Lyon, France.
authors.forename: J
authors.initials: J
authors.lastname: Devillers
mesh_terms: D000465:Algorithms; D001555:Benzene Derivatives / Q000737:chemistry / Q000378:metabolism*; D001673:Biodegradation, Environmental; D016571:Neural Networks, Computer; D013329:Structure-Activity Relationship
chemical_list: D001555:Benzene Derivatives