MedØlDatschgerl

Overview

MedØlDatschgerl (MØD) is a software package developed for graph-based cheminformatics. It includes a general graph transformation system for automatically generating reaction networks from graph grammar formulations of chemistries.

The software is primarily implemented in C++, but the package includes comprehensive Python 3 binding that provides easy access most functionality. The package also includes a large visualisation module that makes it possible to automatically visualise molecules, reactions, and reaction networks. Examples of how to use the Python interface and the visualisation capabilities can be seen in the examples section, and they can all be accessed interactively in the Live Playground.

Source Code and Documentation

Each release is available at GitHub. Please also use GitHub for reporting bugs, suggesting features, and contributing code. The documentation can be found at the GitHub Page.

References

If you use MØD in your research, you may want to cite some of the following papers. You may also be interested in the Graph Grammar Library, which has been used in early versions of MØD.

(As BibTeX)

  • A Software Package for Chemically Inspired Graph Transformation
    Jakob L. Andersen, Christoph Flamm, Daniel Merkle, and Peter F. Stadler. Graph Transformation - 9th International Conference, ICGT 2016, 73-88, 2016 [ DOI | TR ]
  • In silico Support for Eschenmoser’s Glyoxylate Scenario
    Jakob L. Andersen, Christoph Flamm, Daniel Merkle, and Peter F. Stadler. Israel Journal of Chemistry, 55(8):919-933, 2015. [ DOI | TR ]
  • 50 Shades of Rule Composition — From Chemical Reactions to Higher Levels of Abstraction
    Jakob L. Andersen, Christoph Flamm, Daniel Merkle, and Peter F. Stadler. Formal Methods in Macro-Biology, 8738:117-135, 2014. [ DOI ]
  • Conference version: Towards an Optimal DNA-Templated Molecular Assembler
    Jakob L. Andersen, Christoph Flamm, Martin M. Hanczyc, and Daniel Merkle. ALIFE 14: The Fourteenth Conference on the Synthesis and Simulation of Living Systems, 14:557-564, 2014. [ DOI | http ]
    Journal version: Towards Optimal DNA-Templated Computing
    Jakob L. Andersen, Christoph Flamm, Martin M. Hanczyc, and Daniel Merkle. International Journal of Unconventional Computing, 11(3-4):185-203, 2015. [ http ]
  • Generic Strategies for Chemical Space Exploration
    Jakob L. Andersen, Christoph Flamm, Daniel Merkle, and Peter F. Stadler. International Journal of Computational Biology and Drug Design, 7(2/3):225-258, 2014. [ DOI | TR ]
  • Navigating the Chemical Space of HCN Polymerization and Hydrolysis: Guiding Graph Grammars by Mass Spectrometry Data.
    Jakob L. Andersen, Tommy Andersen, Christoph Flamm, Martin M. Hanczyc, Daniel Merkle, and Peter F. Stadler. Entropy, 15(10):4066-4083, 2013. [ DOI | http ]
  • Inferring chemical reaction patterns using rule composition in graph grammars.
    Jakob L. Andersen, Christoph Flamm, Daniel Merkle, and Peter F. Stadler. Journal of Systems Chemistry, 4(1):4, 2013. [ DOI | http ]
  • Maximizing output and recognizing autocatalysis in chemical reaction networks is NP-complete.
    Jakob L. Andersen, Christoph Flamm, Daniel Merkle, and Peter F. Stadler. Journal of Systems Chemistry, 3(1):1, 2012. [ DOI | http ]