MØD

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 bindings that provide easy access to 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 of the documentation. The examples can be explored interactively in the live playground below.

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.

Live Playground

We provide limited access to a server with a MØD installation, for illustrating the examples. When it is online an editor and a read-only terminal will appear in the frame below. The Python snippets from the examples section can be loaded into the editor and edited at will.

To run the code in the editor, press the Run button. You can abort your run with the Kill button. After a successful run, a summary link will appear where you can access a PDF with the figures you have printed. During the run the terminal on the right will show the exact output of running your script, meaning any print calls will show up there.

Note If the frame below is empty the playground server is temporarily offline.

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.

• Chemical Transformation Motifs — Modelling Pathways as Integer Hyperflows
  Jakob L. Andersen, Christoph Flamm, Daniel Merkle, Peter F. Stadler
  IEEE/ACM Transactions on Computational Biology and Bioinformatics, 16(2), 510-523, 2019 [DOI | TR]
  (date of preprint publication: December 2017)
• Rule Composition in Graph Transformation Models of Chemical Reactions
  Jakob L. Andersen, Christoph Flamm, Daniel Merkle, Peter F. Stadler
  MATCH, Communications in Mathematical and in Computer Chemistry, 80(3), 661-704, 2018 [HTTP]
• A Generic Framework for Engineering Graph Canonization Algorithms
  Jakob L. Andersen, Daniel Merkle
  2018 Proceedings of the Twentieth Workshop on Algorithm Engineering and Experiments (ALENEX), 2018 [DOI | TR]
• An Intermediate Level of Abstraction for Computational Systems Chemistry
  Jakob L. Andersen, Christoph Flamm, Daniel Merkle, Peter F. Stadler
  Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 375(2109), 2017 [DOI | TR]
• Chemical Graph Transformation with Stereo-Information
  Jakob L. Andersen, Christoph Flamm, Daniel Merkle, Peter F. Stadler
  Graph Transformation - 10th International Conference, ICGT 2017, 54-69, 2017 [DOI]
• 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]
• 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]
• 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]