Algorithms for molecules, reactions & chemical spaces
The Algorithmic Cheminformatics group at Bielefeld University develops algorithms, models, and software for understanding molecules, chemical reactions, and the structure of chemical spaces.
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Saturation-based atom provenance tracing of the TCA cycle for Oxaloacetate. -
A search tree for graph canonization. From: Andersen, J. L., D. Merkle: A Generic Framework for Engineering Graph Canonicalization Algorithms. Proceedings of the Twentieth Workshop on Algorithm Engineering and Experiments, ALENEX 2018. SIAM, 139-153. 2018. -
A Double Pushout diagram to model a chemical reaction. From: Andersen, J. L., C. Flamm, D. Merkle, P. F. Stadler: Chemical Transformation Motifs — Modelling Pathways as Integer Hyperflows. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 16:2, 510-523, 2019.
Analysing and designing complex chemical networks
We study complex chemical reaction networks across academia, industry, and society. To make sense of their structure and behaviour, we develop computational tools grounded in Algorithmic Cheminformatics: a methodology that integrates chemistry, systems biology, computer science, and mathematics, reaching from theoretical foundations to applications such as metabolic engineering and enzyme design.
Graph algorithms
Molecules are graphs. We design exact and scalable methods for matching, enumeration, and similarity over chemical structures.
Rule-based reaction modelling
We model reactions and reaction networks with rule-based graph transformation — precise, interpretable, and grounded in chemistry.
Open, reproducible software
Our methods ship as documented, tested, open-source tools the community can build on.
In collaboration with the University of Vienna, University of Leipzig, Harvard Medical School, BASF SE, and Thermo Fisher Scientific.
