Graph kernels based on relevant patterns and cycle information for chemoinformatics

Chemoinformatics aim to predict molecule´s properties through informational methods. Computer science´s research fields concerned with chemoinformatics are machine learning and graph theory. From this point of view, graph kernels provide a nice framework for combining these two fields. We present in this paper two contributions to this research field: a graph kernel based on an optimal linear combination of kernels applied to acyclic patterns and a new kernel on the cyclic system of two graphs. These two extensions are validated on two chemoinformatics datasets.