Graph Kernels Based on Relevant Patterns and Cycle Information for Chemoinformatics

Benoit Gaüzère (1) , Luc Brun (1) , Didier Villemin (2) , Myriam Mokhtari (1)

1 Equipe Image - Laboratoire GREYC - UMR6072
2 LCMT - Laboratoire de chimie moléculaire et thioorganique

Chemoinformatics aim to predict molecule's prop- erties through informational methods. Computer sci- ence's research fields concerned with chemoinformat- ics 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 ap- plied to acyclic patterns and a new kernel on the cyclic system of two graphs. These two extensions are vali- dated on two chemoinformatics datasets.

Mots clés: Chemoinformatics; graph kernels; cycles.; cycles

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

Benoit Gaüzère, Luc Brun, Didier Villemin, Myriam Mokhtari. Graph Kernels Based on Relevant Patterns and Cycle Information for Chemoinformatics. International Conference on Pattern Recognition (ICPR) 2012, Nov 2012, Tsukuba, Japan. pp.000-0000. ⟨hal-00768652⟩