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Two new graphs kernels in chemoinformatics

Benoit Gaüzère &
Luc Brun &
Didier Villemin.
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.