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Maximum common subgraph isomorphism algorithms for the matching of chemical structures
John W Raymond1, Peter Willett
1Pfizer Global Research and Development, Ann Arbor Laboratories, 2800 Plymouth Road, Ann Arbor, Michigan 48105, USA. john.raymond@pfizer.com
Journal of Computer-Aided Molecular Design
|January 4, 2003
Summary
This review classifies and analyzes algorithms for the maximum common subgraph (MCS) problem, crucial for chemical structure matching in chemoinformatics. It offers guidance on selecting appropriate MCS algorithms for diverse chemoinformatics applications.
Area of Science:
- Chemoinformatics
- Computational Chemistry
- Graph Theory
Background:
- The maximum common subgraph (MCS) problem is vital for comparing 2D and 3D chemical structures.
- Accurate structure matching is essential for various chemoinformatics tasks.
Purpose of the Study:
- To classify and review existing algorithms for solving the MCS problem.
- To evaluate the applicability of different MCS algorithms to chemoinformatics.
Main Methods:
- Systematic literature review of MCS algorithms.
- Classification of algorithms into exact and approximate methods.
- Analysis of algorithm performance and suitability for chemical structure matching.
Main Results:
- A comprehensive classification of numerous MCS algorithms.
- Identification of strengths and weaknesses of various exact and approximate approaches.
- Recommendations for algorithm selection based on specific chemoinformatics needs.
Conclusions:
- The choice of MCS algorithm significantly impacts the efficiency and accuracy of chemical structure analysis.
- Understanding algorithm characteristics is key to successful application in chemoinformatics.
- Further research can refine MCS algorithms for enhanced chemoinformatics applications.