Chapter open access publication

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

Springer Nature,

Volume 9761, 2016

DOI:10.1007/978-3-319-40530-8_13, Dimensions: pub.1000172341,

Authors

Stadler, Peter F. (1) (3) (4) (5) (6) (7)

* Corresponding author

Affiliations

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  1. (1) University of Vienna, grid.10420.37
  2. (2) University of Southern Denmark, grid.10825.3e, SDU
  3. (3) Fraunhofer Institute for Cell Therapy and Immunology, grid.418008.5
  4. (4) Leipzig University, grid.9647.c
  5. (5) Max Planck Institute for Mathematics in the Sciences, grid.419532.8
  6. (6) Santa Fe Institute, grid.209665.e
  7. (7) University of Copenhagen, grid.5254.6, KU

Description

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.

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University of Southern Denmark

University of Copenhagen

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Research area: Science & Technology

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Research area: Science & Technology

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Times Cited: 1

Field Citation Ratio (FCR): 0.41

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