SpringerProtocols: Abstract: Cell-Based Partitioning

Cell-Based Partitioning

By: Ling Xue³, Florence L. Stahura³, Jürgen Bajorath^{3 4}

Abstract

Full Text

Download PDF (217K)

Partitioning techniques are widely used to classify compound sets or databases according to specific chemical or biological criteria. Partitioning is conceptually related to, yet algorithmically distinct from, conventional clustering methods and is particularly suitable for efficient processing of very large compound sets. Currently, some of the most popular partitioning approaches in the chemoinformatics field involve dimension reduction of initially defined chemistry spaces and creation of subsections of low-dimensional space for molecular classification. These subsections are often called cells. Original chemical reference spaces are generated through selection of various descriptors of molecular structure and properties. Principles and methodological aspects of dimension reduction of chemical spaces and compound partitioning in low-dimensional space are described herein.

Affiliation(s): (3) Computer Aided Drug Discovery, Albany Molecular Research, Inc., Bothell Research Center, Bothell, Washington, USA
(4) Department of Biological Structure, University of Washington, Seattle, Washington, USA

Book Title: Chemoinformatics: Concepts, Methods, and Tools for Drug Discovery

Series: Methods in Molecular Biology | Volume: 275 | Pub. Date: May-12-2004 | Page Range: 279-289 | DOI: 10.1385/1-59259-802-1:279

Subject: Bioinformatics

Key Words: Biological activity - chemical features - chemical space - cluster analysis - compound databases - dimension reduction - molecular descriptors - molecule classification - partitioning algorithms - partitioning in low-dimensional spaces - principal component analysis - visualization

References

Download References

1.	Willett, P., Wintermann, V., and Bawden, D. (1986) Implementation of nonhierarchic cluster analysis methods in chemical information systems: Selection of compounds for biological testing and clustering of substructure search output. J. Chem. Inf. Comput. Sci. 26, 109–118.

2.	Barnard, J. M. and Downs, G. M. (1992) Clustering of chemical structures on the basis of two-dimensional similarity measures. J. Chem. Inf. Comput. Sci. 32, 644–649.

3.	Mason, J. S. and Pickett, S. D. (1997) Partition-based selection. Perspect. Drug Discov. Design 7/8, 85–114.

4.	Stahura, F. L. and Bajorath, J. (2003) Partitioning methods for the identification of active molecules. Curr. Med. Chem. 10, 707–715.

5.	Livingstone, D. J. (2000) The characterization of chemical structures using molecular properties. A survey. J. Chem. Inf. Comput. Sci. 40, 195–209.

6.	Bajorath, J. (2001) Selected concepts and investigations in compound classification, molecular descriptor analysis, and virtual screening. J. Chem. Inf. Comput. Sci. 41, 233–245.

7.	Chen, X., Rusinko, A. III, and Young, S. S. (1998) Recursive partitioning analysis of a large structure-activity data set using three-dimensional descriptors. J. Chem. Inf. Comput. Sci. 38, 1054–1062.

8.	Agrafiotis, D. K., Lobanov, V. S., and Salemme, R. F. (2002) Combinatorial informatics in the post-genomics era. Nature Drug Discov. Rev. 1, 337–346.

9.	Bayley, M. J. and Willett, P. (1999) Binning schemes for partition-based compound selection. J. Mol. Graph. Model. 17, 10–18. <Occurrence Type="DOI"><Handle>10.1016/S1093-3263(99)00016-9</Handle></Occurrence>

10.	Pearlman, R. S. and Smith, K. M. (1998) Novel software tools for chemical diversity. Perspect. Drug Discov. Design 9, 339–353.

11.	Pearlman, R. S. and Smith, K. M. (1999) Metric validation and the receptor-relevant subspace concept. J. Chem. Inf. Comput. Sci. 39, 28–35.

12.	Schnur, D. (1999) Design and diversity analysis of large compound libraries using cell-based methods. J. Chem. Inf. Comput. Sci. 39, 36–45.

13.	Pirard, B. and Pickett, S. D. (2000) Classification of kinase inhibitors using BCUT descriptors. J. Chem. Inf. Comput. Sci. 40, 1431–1440.

14.	Glen, W. G., Dunn, W. J., and Scott, D. R. (1989) Principal component analysis and partial least squares regression. Tetrahedron Comput. Methodol. 2, 349–376. <Occurrence Type="DOI"><Handle>10.1016/0898-5529(89)90004-3</Handle></Occurrence>

15.	Xue, L. and Bajorath, J. (2000) Molecular descriptors for effective classification of biologically active compounds based on principal component analysis identified by a genetic algorithm. J. Chem. Inf. Comput. Sci. 40, 801–809.

16.	Xue, L. and Bajorath, J. (2002) Accurate partitioning of compounds belonging to diverse activity classes. J. Chem. Inf. Comput. Sci. 42, 757–764.

17.	Forrest, S. (1993) Genetic algorithms—principles of natural selection applied to computation. Science 261, 872–878.

18.	Johnson, M. A. and Maggiora, G. M. (eds.) (1990) Concepts and applications of molecular similarity. Wiley, New York, NY.

19.	Bajorath, J. (2002) Integration of virtual and high-throughput screening. Nature Drug Discov. Rev. 1, 337–346.

Comments (Loading...)

Post comment/View all comments