Graph kernel encoding substituents' relative positioning

Benoit Gaüzère 1 Luc Brun 1 Didier Villemin 2
1 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : Chemoinformatics aims to predict molecular properties using informational methods. Computer science's research fields concerned by this domain are machine learning and graph theory. An interesting approach consists in using graph kernels which allow to combine graph theory and machine learning frameworks. Graph kernels allow to define a similarity measure between molecular graphs corresponding to a scalar product in some Hilbert space. Most of existing graph kernels proposed in chemoinformatics do not allow to explicitly encode cyclic information, hence limiting the efficiency of these approaches. In this paper, we propose to define a cyclic representation encoding the relative positioning of substituents around a cycle. We also propose a graph kernel taking into account this information. This contribution has been tested on three classification problems proposed in chemoinformatics.
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Communication dans un congrès
International Conference on Pattern Recognition (ICPR), Aug 2014, Stockholm, Sweden. 6 p., 2014
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Benoit Gaüzère, Luc Brun, Didier Villemin. Graph kernel encoding substituents' relative positioning. International Conference on Pattern Recognition (ICPR), Aug 2014, Stockholm, Sweden. 6 p., 2014. 〈hal-01066389〉

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