title: Neural network classification of mutagens using structural fragment data.
abstract: A neural network was applied to a large, structurally heterogeneous data set of mutagens and non-mutagens to investigate structure-property relationships. Substructural data comprising a total of 1280 fragments were used as inputs. The training of the back-propagation networks was directed by an algorithm which selected an optimal subset of fragments in order to maximize their discriminating power, and a good predictive network. The system comprised three programs: the first used a keyfile of 100 fragments to generate training and test files, the second was the network itself and a procedure for ranking the effectiveness of these fragments and the third randomly replaced the lowest fragments. This cycle was then repeated. After running on a 386/33 PC several networks produced approximately 11% failures in the test set and 6% in the training set. By simplifying the output of the hidden layer it was possible to describe the hidden layer states in terms of clusters of mutagens and non-mutagens. Some of these clusters were structurally homogeneous and contained known mutagenic and non-mutagenic structural classes. This analysis provided a useful means of demonstrating how the network was classifying the data.
authors.affiliation: Research and Laboratory Services Division, Health and Safety Executive, Sheffield S3 7HQ, UK
authors.forename: M
authors.initials: M
authors.lastname: Brinn
authors.forename: P T
authors.initials: PT
authors.lastname: Walsh
authors.forename: M P
authors.initials: MP
authors.lastname: Payne
authors.forename: B
authors.initials: B
authors.lastname: Bott
mesh_terms: D016000:Cluster Analysis; D016208:Databases, Factual; D008958:Models, Molecular; D009153:Mutagens / Q000737:chemistry / Q000145:classification* / Q000633:toxicity; D016571:Neural Networks, Computer; D011897:Random Allocation; D012984:Software; D013329:Structure-Activity Relationship; D009626:Terminology as Topic
chemical_list: D009153:Mutagens