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Chemical Graph Transformation with Stereo-Information

  • Jakob Lykke Andersen
  • Christoph Flamm
  • Daniel Merkle
  • Peter F. Stadler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10373)

Abstract

Double Pushout graph transformation naturally facilitates the modelling of chemical reactions: labelled undirected graphs model molecules and direct derivations model chemical reactions. However, the most straightforward modelling approach ignores the relative placement of atoms and their neighbours in space. Stereoisomers of chemical compounds thus cannot be distinguished, even though their chemical activity may differ substantially. In this contribution we propose an extended chemical graph transformation system with attributes that encode information about local geometry. The modelling approach is based on the so-called “ordered list method”, where an order is imposed on the set of incident edges of each vertex, and permutation groups determine equivalence classes of orderings that correspond to the same local spatial embedding. This method has previously been used in the context of graph transformation, but we here propose a framework that also allows for partially specified stereoinformation. While there are several stereochemical configurations to be considered, we focus here on the tetrahedral molecular shape, and suggest general principles for how to treat all other chemically relevant local geometries. We illustrate our framework using several chemical examples, including the enumeration of stereoisomers of carbohydrates and the stereospecific reaction for the aconitase enzyme in the citirc acid cycle.

Keywords

Double Pushout Chemical graph transformation system Stereochemistry 

Notes

Acknowledgements

This work is supported by the Danish Council for Independent Research, Natural Sciences, the COST Action CM1304 “Emergence and Evolution of Complex Chemical Systems”, and the ELSI Origins Network (EON), which is supported by a grant from the John Templeton Foundation. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the John Templeton Foundation.

Supplementary material

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© Springer International Publishing AG 2017

Authors and Affiliations

  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 FeUSA
  8. 8.Research Network Chemistry Meets MicrobiologyUniversity of ViennaWienAustria
  9. 9.Tokyo Institute of TechnologyEarth-Life Science InstituteTokyoJapan

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