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Related Experiment Videos

Heuristics for chemical compound matching.

Masahiro Hattori1, Yasushi Okuno, Susumu Goto

  • 1Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan. hattori@kuicr.kyoto-u.ac.jp

Genome Informatics. International Conference on Genome Informatics
|February 12, 2005
PubMed
Summary

We developed an efficient algorithm to compare chemical compounds by analyzing their 2D graph structures. This method uses functional groups and heuristics to quickly find similarities, proving effective for biochemical applications.

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Area of Science:

  • Computational Chemistry
  • Bioinformatics
  • Graph Theory

Background:

  • Chemical structure comparison is crucial for drug discovery and understanding biochemical processes.
  • Existing methods may lack efficiency in handling large chemical databases.

Purpose of the Study:

  • To develop an efficient algorithm for comparing chemical compounds based on their 2D graph representations.
  • To incorporate functional group information for biochemically relevant feature detection.
  • To accelerate the identification of maximal common subgraphs using heuristic clique finding.

Main Methods:

  • Representing chemical compounds as 2D graphs (atoms as vertices, bonds as edges).
  • Defining 68 atom types based on chemical environments and functional groups.

Related Experiment Videos

  • Employing heuristic algorithms to accelerate maximal clique finding in association graphs.
  • Applying the algorithm to the KEGG/LIGAND database with adjustable parameters.

    • Main Results:

    • Demonstrated correlation between algorithm parameters, similarity score distribution, and execution time.
    • Showcased the effectiveness of heuristic methods for compound similarity assessment.
    • Validated the algorithm's performance on compound pairs within metabolic pathways.

    Conclusions:

    • The developed algorithm provides an efficient approach for chemical compound comparison.
    • Heuristic clique finding significantly accelerates the detection of common structural features.
    • The method is effective for analyzing biochemical relationships, particularly within metabolic pathways.