Article open access publication

Generic strategies for chemical space exploration.

International Journal of Computational Biology and Drug Design, Inderscience Publishers, ISSN 1756-0756

Volume 7, 2-3, 2014

DOI:10.1504/ijcbdd.2014.061649, Dimensions: pub.1067442257, PMID: 24878732,


Stadler, Peter F (2) (4) (5) (6) (7)



  1. (1) University of Southern Denmark, grid.10825.3e, SDU
  2. (2) Max Planck Institute for Mathematics in the Sciences, grid.419532.8
  3. (3) University of Vienna, grid.10420.37
  4. (4) Fraunhofer Institute for Cell Therapy and Immunology, grid.418008.5
  5. (5) Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, A-1090 Wien, Austria Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, Härtelstraße 16-18, D-04107, Leipzig, Germany; Max Planck Institute for Mathematics in the Sciences, Inselstraße 22 D-04103 Leipzig, Germany; Fraunhofer Institute for Cell Therapy and Immunology, Perlickstraße 1, D-04103 Leipzig, Germany; Center for non-coding RNA in Technology and Health, University of Copenhagen, Grønnegårdsvej 3, DK-1870 Frederiksberg C, Denmark; Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe, NM 87501, USA.
  6. (6) Santa Fe Institute, grid.209665.e
  7. (7) University of Copenhagen, grid.5254.6, KU


The chemical universe of molecules reachable from a set of start compounds by iterative application of a finite number of reactions is usually so vast, that sophisticated and efficient exploration strategies are required to cope with the combinatorial complexity. A stringent analysis of (bio)chemical reaction networks, as approximations of these complex chemical spaces, forms the foundation for the understanding of functional relations in Chemistry and Biology. Graphs and graph rewriting are natural models for molecules and reactions. Borrowing the idea of partial evaluation from functional programming, we introduce partial applications of rewrite rules. A framework for the specification of exploration strategies in graph-rewriting systems is presented. Using key examples of complex reaction networks from carbohydrate chemistry we demonstrate the feasibility of this high-level strategy framework. While being designed for chemical applications, the framework can also be used to emulate higher-level transformation models such as illustrated in a small puzzle game.

Research Categories

Main Subject Area

Links & Metrics

NORA University Profiles

University of Southern Denmark

University of Copenhagen

Dimensions Citation Indicators

Times Cited: 14

Field Citation Ratio (FCR): 5.52

Relative Citation ratio (RCR): 0.58

Open Access Info

Green, Submitted