title: Structure-mutagenicity modelling using counter propagation neural networks.
abstract: The set of 95 aromatic amines and their mutagenic potency was treated with counter propagation neural network, which enables analysis of self-organising maps (SOMs) and also the prediction of mutagenicity. Compounds were described with four classes of descriptors: topostructural (TS), topochemical (TC), geometrical, and quantum chemical (QC). The models were tested on their prediction ability with leave-one-out (LOO) cross-validation method. The squares of correlation coefficient lie between 0.65 and 0.75 and are comparable with models obtained by linear methods. In addition, we analysed self-organising maps and found clusters of structurally similar compounds.
authors.affiliation: Laboratory for Chemometrics, National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia.
authors.forename: Marjan
authors.initials: M
authors.lastname: Vracko
authors.forename: Denise
authors.initials: D
authors.lastname: Mills
authors.forename: Subhash C
authors.initials: SC
authors.lastname: Basak