Relevant Cycle Hypergraph Representation for Molecules

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

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

Chemoinformatics aims to predict molecule's properties through informational methods. Some methods base their prediction model on the comparison of molecular graphs. Considering such a molecular representation, graph kernels provide a nice framework which allows to combine machine learning techniques with graph theory. Despite the fact that molecular graph encodes all structural information of a molecule, it does not explicitly encode cyclic information. In this paper, we propose a new molecular representation based on a hypergraph which explicitly encodes both cyclic and acyclic information into one molecular representation called relevant cycle hypergraph. In addition, we propose a similarity measure in order to compare relevant cycle hypergraphs and use this molecular representation in a chemoinformatics prediction problem.

Domaines: Apprentissage [cs.LG]

Benoit Gaüzère, Luc Brun, Didier Villemin. Relevant Cycle Hypergraph Representation for Molecules. 9th IAPR-TC-15 Graph-Based Representations in Pattern Recognition, May 2013, Austria. pp.111. ⟨hal-00829227⟩