Two new graphs kernels in chemoinformatics

Chemoinformatics is a well established research field concerned with the discovery of molecule’s properties through informational techniques. Computer science’s research fields mainly concerned by chemoinformatics are machine learning and graph theory. From this point of view, graph kernels provide a nice framework combining machine learning and graph theory techniques. Such kernels prove their efficiency on several chemoinformatics problems and this paper presents two new graph kernels applied to regression and classification problems. The first kernel is based on the notion of edit distance while the second is based on subtrees enumeration. The design of this last kernel is based on a variable selection step in order to obtain kernels defined on parsimonious sets of patterns. Performances of both kernels are investigated through experiments.

► Definition of a semi definite positive graph kernel based on edit distance.
► Definition of a graph kernel based on the explicit enumeration of tree patterns.
► Selection of relevant tree patterns.
► Explicit enumeration of tree patterns allows us to optimize the graph description.