Clustering chemical databases using adaptable projection cells and MCS similarity values

Journal of Chemical Information and Modeling

Volumn 45, Issue 5, 2005, Pages 1178-1194

  Ruiz, Irene Luque ^a   García, Gonzalo Cerruela ^a   Gómez Nieto, Miguel Ángel ^a  

^a UNIVERSITY OF CÓRDOBA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL METHODS; GRAPH THEORY; PROBLEM SOLVING;

CHEMICAL DATABASES; MOLECULAR GRAPHS; SCREENING PROCESSES; SUBSTRUCTURES;

DATABASE SYSTEMS;

EID: 26944454840     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci050035o     Document Type: Article

References (19)

1. Cundari, T. R.; Russo, M. Database Mining Using Soft Computing Techniques. An Integrated Neural Network-Fuzzy Logic Genetic Algorithm Approach. J. Chem. Inf. Comput. Sci. 2001, 41, 281-287.
2. Kantardzic, M. Data Mining: Concepts, Models, Methods, and Algorithms; Wiley-IEEE Computer Society Pr: 2002.
3. Kaufman, L.; Rousseeew P. J. Finds Group in Data: An Introduction to Clustering Analysis; John Wiley & Sons: 1990.
4. Downs, G. M.; Barnard, J. M. Clustering and Their Uses In Computational Chemistry. In Reviews in Computational Chemistry; Lipkowitz, K. B., Boyd, D. B., Eds.; Wiley-VCH: New York, 2003; Vol. 18, pp 1-39.
5. Gillet, V. J.; Downs, G. M.; Ling, A.; Lynch, M. F.; Venkataran, P.; Wood, J. V.; Dethlefsen, W. Computer Storage and Retrieval of Generic Chemical Structures in Patents. 8. Reduced Chemical Graph and Their Applications in Generic Chemical Structure Retrieval. J. Chem. Inf. Comput. Sci. 1987, 27, 126-137.
6. Butina, D. Unsupervised Database Clustering Based on Daylight's Fingerprint and Tanimoto Similarity: A Fast and Automated Way to Cluster Small and Large Data Sets. J. Chem. Inf. Comput. Sci. 1999, 39 (4), 747-750.
7. Turner, D. B.; Tyrrel, S. M.; Willett, P. Rapid Quantification of Molecular Diversity for Selective Database Acquisition. J. Chem. Inf. Comput. Sci. 1997, 37, 18-22.
8. Cerruela Garcia, G.; Luque Ruiz, I.; Gómez-Nieto, M. A. Step-by-Step Calculation of All Maximum Common Substructures Through a Constraint Satisfaction based Algorithm. J. Chem. Inf. Comput. Sci. 2004, 44 (1), 30-41.
9. Raymond, J. W.; Gardiner, E. J.; Willett, P. Heuristics for Similarity Searching of Chemical Graphs Using a Maximum Common Edge Subgraph Algorithm. J. Chem. Inf. Comput. Sci. 2002, 42 (2), 305-316.
10. Rouvray, D. H.; Balaban, A. T. Chemical Applications of Graph Theory. Applications of Graph Theory; Wilson, R. J., Beineke, L. W., Eds.; Academic Press: 1979; pp 177-221.
11. Willett, P.; Barnard, J. M.; Downs, G. Chemical Similarity Searching. J. Chem. Inf. Comput. Sci. 1998, 38 (6), 983-996.
12. Taraviras, S. L.; Ivanciuc, O.; Carbol-Bass, D. Identification of Groupings of Graph Theoretical Molecular Descriptors Using a Hybrid Cluster Analysis Approach. J. Chem. Inf. Comput. Sci. 2000, 40 (5), 1128-1146.
13. SPECS and BioSPECS B.V. http://www.specs.net.
14. Everett, B. S.; Landau, S.; Leese, M. Cluster Analysis; Arnold Publishers: 2001.
15. Jajuga, K.; Sokoowski, A.; Hermann Bock, A. Classification, Clustering and Data Analysis; Springer-Verlag: 2002.
16. MDL Information Systems, Inc. http://www.mdli.com.
17. The MathWorks, Inc. http://www.mathworks.com.
18. Cerruela Garcia, G.; Luque Ruiz, I.; Gómez-Nieto, M. A. Clustering Chemical Databases Through Projection of MOS Similarity Measures on Multidimensional Spaces. In Lectures Series on Computer and Computational Sciences; VSP International Science Publisher: 2004; Vol. 1, 181-184.
19. Cerruela García, G.; Luque Ruiz, I.; Gómez-Nieto, M. A. Representation of the Molecular Topology of Cyclical Structures by means of Cycle Graphs: 2. Application to Chemical Databases J. Chem. Inf. Comput. Sci. 2004, 44 (4), 1383-1393.