Modeling of structure–mutagenicity relationships: counter propagation neural network approach using calculated structural descriptors

Counter propagation artificial neural network was applied for modeling the mutagenicity of 95 aromatic and heteroaromatic amines collected from the literature. Molecules were represented by topostructural, topochemical, geometrical and quantum chemical descriptors. A sphere exclusion algorithm was used for rational division of the dataset into training and test sets. The initial training set was improved by a step-wise inclusion of two outliers from the test set. Recall ability of the final model is good (R^2=0.986) as well as prediction ability in respect to the test set (R^2=0.816). Validity of the best model obtained in the study was confirmed by randomization test and test with exchanged training and test sets. Study demonstrated the capabilities of CP ANN in analyzing the similarities between compounds and identifying of outliers. It was shown that CP ANN is a powerful tool for modeling the structure–mutagenicity relationships of the compounds considered.