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  • Poster presentation
  • Open Access

Creating chemo- & bioinformatics workflows, further developments within the CDK-Taverna Project

  • 1, 2,
  • 1 and
  • 1
Chemistry Central Journal20082 (Suppl 1) :P27

  • Published:


  • Chemical Substance
  • Soft Computing
  • Chemistry Development
  • Current Development
  • Computing Framework

The CDK-Taverna project aims at building an open-source pipelining solution through combination of different open-source projects such as Taverna [1], the Chemistry Development Kit (CDK) [2] and Bioclipse [3].

Pipelining or workflow tools allow for the Lego™-like, graphical assembly of I/O modules and algorithms into a complex workflow which can be easily deployed, modified and tested without the hassle of implementing it into a monolithic application.

Current developments in CDK-Taverna focus on a soft computing framework which allows a flexible use of different methods from, for example, the WEKA [4] library. Here, properties of chemical substances may be calculated using descriptors from the QSAR / QSPR package of the Chemistry Development Kit (CDK).

Further, a reaction enumeration algorithm for combinatorial chemistry based on existing methods of the Chemistry Development Kit is being developed. This algorithm allows for the enumeration of a reaction given that reactants and products are provided as “Markush” structures.

Authors’ Affiliations

Cologne University Bioinformatics Center (CUBIC), Cologne, Germany
University of Applied Sciences of Gelsenkirchen, Institute for Bioinformatics and Chemoinformatics, Recklinghausen, Germany


  1. Oinn T, Addis M, Ferris M, Marvin D, Senger M, Greenwood M, Carver T, Glover K, Pocock M, Wipat A, Li P: Taverna: A tool for the composition and enactment of bioinformatics workflows. Bioinformatics. 2004, 20 (17): 3045-3054. 10.1093/bioinformatics/bth361.View ArticleGoogle Scholar
  2. Steinbeck C, Han YQ, Kuhn S, Horlacher O, Luttmann E, Willighagen E: The Chemistry Development Kit (CDK): An open-source Java library for chemo- and bioinformatics. J Chem Inf Comput Sci. 2003, 43: 493-500. 10.1021/ci025584y.View ArticleGoogle Scholar
  3. Spjuth O, Helmus T, Willighagen EL, Kuhn S, Eklund V, et al: An open rich client workbench for chemo- and bioinformatics. submitted.,Google Scholar
  4. Witten IH, Frank E: Data-Mining Practical machine learning tools and techniques. 2005, Morgen Kaufmann, San Francisco, 2nd EditionGoogle Scholar
  5. Hassan M, Brown RB, Varma-O'Brien , Rogers D: Cheminformatics analysis and learning in a data pipelining environment. Molecular Diversity. 2006, 10: 283-299. 10.1007/s11030-006-9041-5.View ArticleGoogle Scholar


© Kuhn et al. 2008