This work deals with the application of graph kernel methods to the prediction of molecular properties. In this document, we first present a state of the art of graph kernels used in chemoinformatics and particurlarly those which are based on bags of patterns. Within this framework, we introduce the treelet kernel based on a set of trees which allows to encode most of the structural information encoded in molecular graphs. We also propose a combination of this kernel with multiple kernel learning methods in order to extract a subset of relevant patterns. This kernel is then extended by including cyclic information using two molecular representations defined by the relevant cycle graph and the relevant cycle hypergraph. Relevant cycle graph allows to encode the cyclic system of a molecule whereas the relevant cycle hypergraph allows to encode the cyclic system and its adjacency relationships with acyclic parts of the molecule. We also propose two kernels dealing with this two molecular representations. Finally, the last part aims to define two graph kernels based on graph edit distance. The first one is based on a regularisation operator using graph edit distance. The second one allows to compare non-isomorphic treelets thanks to an algorithm computing a treelet edit distance.
