ARTIFICIAL NEURAL NETWORKS APPLIED TO CLASSIFICATION OF MUTAGENIC ACTIVITY OF NITROSUBSTITUTED POLYCYCLIC AROMATIC-HYDROCARBONS

Three-layer artificial neural networks (ANN) with back-propagation of error have been applied to classification of nitro-substituted polycyclic aromatic hydrocarbons (NPAH) based on the regularity that the structure difference of NPAH compounds leads to different mutagenic activity towards Salmonella typhimurium. The network's architecture and parameters were optimized to give maximum correct classification rate of 93.8% for two different classes of NPAHs: weakly active and strongly active ones. The results compared favourably with those obtained by nonlinear mapping pattern recognition method. Considering that the most important factor for NPAH's mutagenicity might be the electron effect of the substituted nitro-groups, an electrotopological state index of nitrogen atom was introduced additionally as one of network's inputs, and the correct classification rate was consequently raised to 97.5%. The network's prediction ability for untrained samples was also satisfactory.