A non-standard view on artificial neural networks

• EBP ANN, Kohonen ANN, CP ANN, bottle-neck, decision hierarchy, mem-computers
• ANN are suitable tool for modeling, clustering and classification
• ANN are used for QSAR modeling and read across
• As a case study a QSAR and classification/clustering models for fish toxicity are presented

In the paper first the two main learning strategies of the artificial neural networks (ANNs), the error-back propagation (EBP) and Kohonen self-organizing maps (SOM) are briefly described. Next, two nonstandard network layouts of the ANNs (bottle-neck and pyramidal decision tree) one for each of both learning strategies are suggested. In the last part, the use of counter-propagation (CP) ANN for handling chemical structures in QSAR modeling, classification and clustering is discussed. These concepts are of particular interest for the computer-aided drug research and in computer-aided toxicology. In the case study the results of fish toxicity research are described.