Anzeige
Erschienen in:
2014 | OriginalPaper | Buchkapitel
Incorporating Molecule’s Stereisomerism within the Machine Learning Framework
verfasst von : Pierre-Anthony Grenier, Luc Brun, Didier Villemin
Erschienen in: Structural, Syntactic, and Statistical Pattern Recognition
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
An important field of chemoinformatics consists in the prediction of molecule’s properties, and within this field, graph kernels constitute a powerful framework thanks to their ability to combine a natural encoding of molecules by graphs, with classical statistical tools. Unfortunately some molecules encoded by a same graph and differing only by the three dimensional orientation of their atoms in space have different properties. Such molecules are called stereoisomers. These latter properties can not be predicted by usual graph methods which do not encode stereoisomerism. In this paper we propose to encode the stereoisomerism property of each atom of a molecule by a local subgraph. A kernel between bags of such subgraphs provides a similarity measure incorporating stereoisomerism properties. We then propose two extensions of this kernel incorporating in each sub graph information about its surroundings.