Prediction models for absorption, distribution, metabolic and excretion properties of chemical compounds play a crucial rule in the drug discovery process. Often such models are derived via machine learning techniques. Kernel based learning algorithms, like the well known support vector machine (SVM) have gained a growing interest during the last years for this purpose. One of the key concepts of SVMs is a kernel function, which can be thought of as a special similarity measure. In this Chapter the author describes optimal assignment kernels for multi-labeled molecular graphs. The optimal assignment kernel is based on the idea of a maximal weighted bipartite matching of the atoms of a pair of molecules. At the same time the physico-chemical properties of each single atom are considered as well as the neighborhood in the molecular graph. Later on our similarity measure is extended to deal with reduced graph representations, in which certain structural elements, like rings, donors or acceptors, are condensed in one single node of the graph. Comparisons of the optimal assignment kernel with other graph kernels as well as with classical descriptor based models show a significant improvement in prediction accuracy.
Clustering Uncertain Data Objects using Jeffreys-Divergence and Maximum Bipartite Matching based Similarity Measure