ICGT 2016: Graph Transformation pp 206-222

Automatic Inference of Graph Transformation Rules Using the Cyclic Nature of Chemical Reactions

  • Christoph Flamm
  • Daniel Merkle
  • Peter F. Stadler
  • Uffe Thorsen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9761)

Abstract

Graph transformation systems have the potential to be realistic models of chemistry, provided a comprehensive collection of reaction rules can be extracted from the body of chemical knowledge. A first key step for rule learning is the computation of atom-atom mappings, i.e., the atom-wise correspondence between products and educts of all published chemical reactions. This can be phrased as a maximum common edge subgraph problem with the constraint that transition states must have cyclic structure. We describe a search tree method well suited for small edit distance and an integer linear program best suited for general instances and demonstrate that it is feasible to compute atom-atom maps at large scales using a manually curated database of biochemical reactions as an example. In this context we address the network completion problem.

Keywords

Chemistry Atom-atom mapping Maximum common edge subgraph Integer linear programming Network completion 

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Christoph Flamm
    • 2
    • 8
  • Daniel Merkle
    • 1
  • Peter F. Stadler
    • 2
    • 3
    • 4
    • 5
    • 6
    • 7
  • Uffe Thorsen
    • 1
  1. 1.Department of Mathematics and Computer ScienceUniversity of Southern DenmarkOdenseDenmark
  2. 2.Institute for Theoretical ChemistryUniversity of ViennaWienAustria
  3. 3.Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for BioinformaticsUniversity of LeipzigLeipzigGermany
  4. 4.Max Planck Institute for Mathematics in the SciencesLeipzigGermany
  5. 5.Fraunhofer Institute for Cell Therapy and ImmunologyLeipzigGermany
  6. 6.Center for Non-coding RNA in Technology and HealthUniversity of CopenhagenFrederiksbergDenmark
  7. 7.Santa Fe InstituteSanta Fe NmUSA
  8. 8.Research Network Chemistry Meets MicrobiologyUniversity of ViennaWienAustria

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