A Fuzzy Kohonen SOM Implementation and Clustering of Bio-active Compound Structures for Drug Discovery

Hierarchical methods like Ward's and group average (Gave) and nonhierarchical methods like Jarvis Patrick's and k-means are preferred methods to cluster a diverse set of compounds for a number of drug targets (using fingerprints based descriptors). In this work the applications of fuzzy Kohonen neural network and other self-organizing map (SOM) algorithms to the clustering of chemical datasets are investigated. The self-organizing maps networks usually possess a number of parameters such as the learning rate and neighborhood size, which are heuristically selected; whilst in the fuzzy self-organizing map, an optimization technique which automatically selects the best parameters using the fuzzy membership functions has been used. The results of the fuzzy SOM neural network are evaluated in comparison with other SOM neural networks (namely Kohonen, neural gas and enhanced neural gas), Wards and group average methods for the clustering of different biologically active chemical structures using topological descriptors. The results show that the performance of fuzzy SOM method not only outperforms other SOM networks with the best heuristics, but also give better results than the Wards and group average methods