Similarity-potency trees: A method to search for SAR information in compound data sets and derive SAR rules

Journal of Chemical Information and Modeling

Volumn 50, Issue 8, 2010, Pages 1395-1409

  Wawer, Mathias ^a   Bajorath, Jürgen ^a  

^a UNIVERSITY OF BONN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

AIR NAVIGATION;

CLUSTERING TECHNIQUES; HIGH THROUGHPUT SCREENING; INFORMATION CONTENTS; INTERACTIVE ANALYSIS; INTERPRETABILITY; LEAD OPTIMIZATION; PRACTICAL GUIDANCE; SCIENTIFIC COMMUNITY;

TREES (MATHEMATICS);

ARTICLE; CHEMISTRY; CLUSTER ANALYSIS; DRUG DESIGN; METHODOLOGY; MOLECULAR LIBRARY; STRUCTURE ACTIVITY RELATION;

CHEMISTRY, PHARMACEUTICAL; CLUSTER ANALYSIS; DRUG DESIGN; SMALL MOLECULE LIBRARIES; STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 77956021620     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci100197b     Document Type: Article

References (32)

1. Kubinyi, H. Similarity and Dissimilarity. A medicinal chemists view Perspect. Drug Discovery Des. 1998, 9-11, 225-252
2. Gribbon, P.; Lyons, R.; Laflin, P.; Bradley, J.; Chambers, C.; Williams, B. S.; Kighley, W.; Sewing, A. Evaluating Real-Life High-Throughput Screening Data J. Biomol. Screening 2005, 10, 99-107
3. Kubinyi, H. QSAR and 3D QSAR in Drug Design. Part 1: Methodology Drug Discovery Today 1997, 2, 457-467
4. Esposito, E. X.; Hopfinger, A. J.; Madura, J. D. Methods for Applying the Quantitative Structure-Activity Relationship Paradigm Methods Mol. Biol. 2004, 275, 131-214
5. Malo, N.; Hanley, J. A.; Cerquozzi, S.; Pelletier, J.; Nadon, R. Statistical Practice in High-Throughput Data Analysis Nat. Biotechnol. 2006, 24, 167-175
6. Ahlberg, C. Visual Exploration of HTS Databases: Bridging the Gap between Chemistry and Biology Drug Discovery Today 1999, 4, 270-485
7. Roberts, G.; Myatt, G. J.; Johnson, W. P.; Cross, K. P.; Blower, P. E., Jr. Lead Scope: Software for Exploring Large Sets of Screening Data J. Chem. Inf. Comput. Sci. 2000, 40, 1302-1314
8. Stahl, M.; Mauser, H. Database Clustering with a Combination of Fingerprint and Maximum Common Substructure Methods J. Chem. Inf. Model. 2005, 45, 542-548
9. Böcker, A. Toward an Improved Clustering of Large Data Sets Using Maximum Common Substructures and Topological Fingerprints J. Chem. Inf. Model. 2008, 48, 2097-2107
10. Agrafiotis, D.; Shemanarev, M.; Connolly, P.; Farnum, M.; Lobanov, V. SAR Maps: A New SAR Visualization Technique for Medicinal Chemists J. Med. Chem. 2007, 50, 5926-5937
11. Kolpak, J.; Connolly, P. J.; Lobanov, V. S.; Agrafiotis, D. K. Enhanced SAR Maps: Expanding the Data Rendering Capabilities of a Popular Medicinal Chemistry Tool J. Chem. Inf. Model. 2009, 49, 2221-2230
12. Wilkens, S. J.; Janes, J.; Su, A. I. HierS: Hierarchical Scaffold Clustering Using Topological Chemical Graphs J. Med. Chem. 2005, 48, 3182-93
13. Cho, S. J.; Sun, Y. Visual exploration of structure-activity relationship using maximum common framework J. Comput.-Aided Mol. Des. 2008, 22, 571-8
14. Shanmugasundaram, V.; Maggiora, G. M. Characterizing Property and Activity Landscapes Using an Information-Theoretic Approach. Proceedings of the 222nd American Chemical Society National Meeting, Division of Chemical Information, Chicago, IL, August 26-30, 2001; American Chemical Society: Washington, D.C., 2001; abstract no. 77.
15. Peltason, L.; Bajorath, J. SAR Index: Quantifying the Nature of Structure-Activity Relationships J. Med. Chem. 2007, 50, 5571-5578
16. Guha, R.; Van Drie, J. H. Structure-Activity Landscape Index: Identifying and Quantifying Activity Cliffs J. Chem. Inf. Model 2008, 48, 646-658
17. Bajorath, J.; Peltason, L.; Wawer, M.; Guha, R.; Lajiness, M. S.; Van Die, J. H. Navigating Structure-Activity Landscapes Drug Discovery Today 2009, 14, 698-705
18. Wawer, M.; Peltason, L.; Weskamp, N.; Teckentrup, A.; Bajorath, J. Structure-Activity Relationship Anatomy by Network-like Similarity Graphs and Local Structure-Activity Relationship Indices J. Med. Chem. 2008, 51, 6075-6084
19. Wawer, M.; Sun, S.; Bajorath, J. Computational Characterization of SAR Microenvironments in High-Throughput Screening Data. Intl. J. High Throughput Screen. 2010, 1, 15-27.
20. Maggiora, G. M. On Outliers and Activity Cliffs-Why QSAR often Disappoints J. Chem. Inf. Model. 2006, 46, 1535-1535
21. Wawer, M.; Peltason, L.; Bajorath, J. Elucidation of Structure-Activity Relationship Pathways in Biological Screening Data J. Med. Chem. 2009, 52, 1075-1080
22. Wawer, M.; Bajorath, J. Systematic Extraction of Structure-Activity Relationship Information from Biological Screening Data ChemMedChem 2009, 4, 1431-1438
23. Liu, T.; Lin, Y.; Wen, X.; Jorissen, R. N.; Gilson, M. K. BindingDB: a Web-Accessible Database of Experimentally Determined Protein-Ligand Binding Affinities Nucleic Acids Res. 2007, 35, D198-D201
24. PubChem; National Center for Biotechnology Information: Bethesda, MD; http://pubchem.ncbi.nlm.nih.gov. Accessed January 05, 2010.
25. Willett, P.; Barnard, J. M.; Downs, G. M. Chemical Similarity Searching J. Chem. Inf. Comput. Sci. 1998, 38, 983-996
26. Rogers, D.; Brown, R. D.; Hahn, M. Using Extended-Connectivity Fingerprints with Laplacian-modified Bayesian analysis in High-Throughput Screening Follow-up J. Biomol. Screening 2005, 10, 682-686
27. Scitegic Pipeline Pilot, Student ed.; version 6.1; Accelrys, Inc.: San Diego, CA, 2007.
28. OMadadhain, J.; Fisher, D.; White, S. Java Universal Network/Graph Framework; Berkeley Software Distribution: University of California, Berkeley; http://jung.sourceforge.net/. Accessed January 11, 2010.
29. Fry, B.; Reas, C. Processing, version 1.0, 2009; http://processing.org. Accessed January 11, 2010.
30. Rusinko, A., III; Farmen, M. W.; Lambert, C. G.; Brown, P. L.; Young, S. S. Analysis of a large structure/biological activity data set using recursive partitioning J. Chem. Inf. Comput. Sci. 1999, 39, 1017-26
31. R Development Core Team; R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2009.
32. Ward, J. H. Hierarchical Grouping to Optimize an Objective Function J. Am. Stat. Assoc. 1963, 58, 236-244