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Incorporating Molecule’s Stereisomerism within the Machine Learning Framework

  • Pierre-Anthony Grenier
  • Luc Brun
  • Didier Villemin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8621)

Abstract

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.

Keywords

Asymmetric Carbon Support Vector Regression Machine Graph Kernel Adjacency Relationship Dimensional Orientation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Pierre-Anthony Grenier
    • 1
  • Luc Brun
    • 1
  • Didier Villemin
    • 2
  1. 1.GREYC UMR CNRS 6072CaenFrance
  2. 2.LCMT UMR CNRS 6507CaenFrance

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