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

Christoph Flamm, Daniel Merkle, Peter F. Stadler, Uffe Thorsen

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Abstrakt

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.
OriginalsprogEngelsk
TitelGraph Transformation : Proceedings of the 9th International Conference on Graph Transformation
RedaktørerRachid Echahed, Mark Minas
ForlagSpringer
Publikationsdato2016
Sider206-222
ISBN (Trykt)978-3-319-40529-2
ISBN (Elektronisk) 978-3-319-40530-8
DOI
StatusUdgivet - 2016
Begivenhed9th International Conference on Graph Transformation - Vienna, Østrig
Varighed: 5. jul. 20166. jul. 2016
Konferencens nummer: 9
https://sites.google.com/site/icgt2016/

Konference

Konference9th International Conference on Graph Transformation
Nummer9
LandØstrig
ByVienna
Periode05/07/201606/07/2016
Internetadresse
NavnLecture Notes in Computer Science
Vol/bind9761
ISSN0302-9743

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Citationsformater

Flamm, C., Merkle, D., Stadler, P. F., & Thorsen, U. (2016). Automatic Inference of Graph Transformation Rules Using the Cyclic Nature of Chemical Reactions. I R. Echahed, & M. Minas (red.), Graph Transformation: Proceedings of the 9th International Conference on Graph Transformation (s. 206-222). Springer. Lecture Notes in Computer Science, Bind. 9761 https://doi.org/10.1007/978-3-319-40530-8_13