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A classification of biochemical compounds based on their primitive structures and graph kernels

The data encoded in the form of a graph are widely used in many fields of study. In cheminformatics, the data of biochemical compounds can be encoded as a graph where its vertices are atoms, and its edges are chemical bonds between pairs of atoms. By this representation, we can identify the molecular functions of a labeled biochemical compound with the graph classification methods which most of them depend on graph kernel. In this study, we proposed an algorithm to extract primitive substructures of the compounds and encode them in a new form of graph for feeding them a support vector machine with a graph kernel. The algorithm was tested with four data sets and yield the average accuracy of 0.847. Furthermore, the performance of the algorithm was also better than that of the solely graph kernels without primitive substructure extraction. All in all, the extraction of the primitive substructures can be a great tool in extracting compound structure features in biochemical compound classification.