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Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods
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SIMCOMP/SUBCOMP: chemical structure search servers for network analyses.

Masahiro Hattori1, Nobuya Tanaka, Minoru Kanehisa

  • 1Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan.

Nucleic Acids Research
|May 13, 2010
PubMed
Summary

Bioinformatics tools SIMCOMP and SUBCOMP link chemical structures to metabolic pathways. These servers enable understanding biological functions by connecting genomic and chemical data.

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Area of Science:

  • Bioinformatics
  • Cheminformatics
  • Systems Biology

Background:

  • Understanding the link between genomic and chemical aspects of metabolic pathways is a key bioinformatics challenge.
  • Existing methods may not fully capture the complex relationships between chemical structures and biological functions within metabolic networks.

Purpose of the Study:

  • To introduce and describe two chemical structure search servers, SIMCOMP and SUBCOMP.
  • To demonstrate how these servers facilitate the interpretation of chemical structures within the context of biological pathways and functions.

Main Methods:

  • SIMCOMP utilizes a graph-based approach to find maximal common subgraph isomorphism via maximal cliques.
  • SUBCOMP employs an extended method to address the subgraph isomorphism problem.
  • Both servers integrate with the KEGG PATHWAY and BRITE databases.

Main Results:

  • SIMCOMP and SUBCOMP provide searchable databases for chemical similarity and substructure searches, respectively.
  • The servers successfully link chemical structures to entries in KEGG PATHWAY and BRITE.
  • This integration allows for the interpretation of chemical structures' biological relevance.

Conclusions:

  • SIMCOMP and SUBCOMP are valuable tools for bioinformatics research, bridging chemical and genomic information.
  • These servers enhance the understanding of metabolic networks and associated biological functions.
  • The ability to link chemical structures to biological databases aids in deciphering the significance of molecular components in biological systems.