A family of ring system-based structural fragments for use in structure - Activity studies: Database mining and recursive partitioning

Journal of Chemical Information and Modeling

Volumn 46, Issue 3, 2006, Pages 1069-1077

  Nilakantan, Ramaswamy ^a   Nunn, David S ^a   Greenblatt, Lynne ^a   Walker, Gary ^a   Haraki, Kevin ^a   Mobilio, Dominick ^a  

^a WYETH RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA ACQUISITION; DATA MINING; LIBRARIES; SEARCH ENGINES; TREES (MATHEMATICS);

COMBINATORIAL LIBRARY DESIGN; FAST COMPOUND CLUSTERING; RECURSIVE PARTITIONING; STRUCTURAL DESCRIPTORS;

DATABASE SYSTEMS;

ARTICLE; DRUG DESIGN; INFORMATION RETRIEVAL; STRUCTURE ACTIVITY RELATION;

DRUG DESIGN; INFORMATION STORAGE AND RETRIEVAL; STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 33745351349     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci050521b     Document Type: Article

References (32)

1. Hodes. L.: Hazard. G. F.: Geran, R. I.; Richman S A Statistical Heuristic Method for Automated Selection of Drugs for Screning. J. Med. Chem. 1977, 20, 469-475.
2. Carhart, R. E.; Smith, D. H.; Venkataraghavan, R. Atom-Pairs as Molecular Features in Structure - Activity Studies: Definition and Applications. J. Chem. Inf. Comput. Sci. 1985, 25, 64-73.
3. Nilakantan, R.; Bauman, N.; Dixon, J. S.; Venkataraghavan, R. Topological Torsion: A New Molecular Descriptor for SAR Applications: Comparison with Other Descriptors. J. Chem. Inf. Comput. Sci. 1987, 27, 82-85.
4. MACCS keys are a product of Elsevier MDL, San Leandro, CA.
5. James, C. A.; Weininger, D.; Delany, J. In Daylight Theory: Fingerprints', Daylight Chemical Information Systems Inc.
6. UNITY fingerprints are a product of Tripos Inc.: St. Louis, MO.
7. BCI fingerprints are a product of Barnard Chemical Information Systems Ltd. Sheffield, U.K.
8. Willett, R. Chemical Similarity Searching. J. Chem. Inf. Comput. Sci. 1998, 38, 983-996.
9. Nilakantan, R.; Bauman, N.; Haraki, K. S.; Venkataraghavan, R. A Ring-Based Structural Query System: Use of a Novel Ring-Complexity Heuristic. J. Chem. Inf. Comput. Sci. 1990, 30, 65-68.
10. Nilakantan, R.; Bauman, N.; Haraki, K. S. Database Diversity assessment: New Ideas, Concepts and Tools. J. Comput-Aided Mol. Des. 1997, 11, 447-452.
11. Nilakantan, R.; Immermann, N.; Haraki, K. S. A Novel Approach to Combinatorial Library Design. Comb. Chem. High-Throughput Screening 2002, 5, 105-110.
12. Nilakantan, R.; Nunn, D. S. A Fresh Look at Pharmaceutical Screening Library Design. Drug Discovery Today 2003, 8, 668-672.
13. Bemis, G. W.; Murcko, M. A. Properties of Known Drugs: Molecular Frameworks. J. Med. Chem. 1996, 39, 2887-2893.
14. Bemis, G.W.; Murcko, M. A. Properties of Known Drugs: Sidechains. J. Med. Chem. 1999, 42, 5095-5099.
15. Xu, J. A New Approach to Finding Natural Chemical Structure Classes. J. Med. Chem. 2002, 45, 5311-5320.
16. Roberts, R.; Myatt, G. J.; Johnson, W. P.; Cross, K. P.; Blower Jr., P. E. LeadScope: Software for Exploring Large Sets of Screening Data. J. Chem. Inf. Comput. Sci. 2000, 40, 1302-1314.
17. Xu, Y.-J.; Johnson, M. Algorithm for Naming Molecular Equivalence Classes Represented by Labeled Pseudographs. J. Chem. Inf. Comput. Sci. 2001, 41, 181-185.
18. Xu, Y.-J.; Johnson, M. Using Molecular Equivalence Numbers to Visually Explore Structural Features that Distinguish Chemical Libraries. J. Chem. Inf. Comput. Sci. 2002, 42, 912-926.
19. Weininger, D. SMILES, A Chemical Language and Information System. 1. Introduction to Methodology and Encoding Rules. J. Chem. Inf. Comput. Sci. 1988, 28, 31.
20. Hawkins, D. M.; Young, S. S.; Rusinko III, A. Analysis of a Large Structure - Activity Data Set using Recursive Partitioning. Quant. Struct.-Act. Relat. 1997, 16, 296-302.
21. Rusinko III, A.; Farmen, M. W.; Lambert, C. G.; Brown, P. L.; Young, S. S. J. Chem. Inf. Comput. Sci. 1999, 39, 1017-1026.
22. van Rhee, A. M.; Stocker, J.; Printzenhoff, D.; Creech, C.; Wagoner, P. K.; Spear, K. L. Retrospective Analysis of an Experimental High-Throughput Screening Data Set by Recursive Partitioning. J. Comb. Chem. 2001, 3, 267-277.
23. van Rhee, A. M. Use of Recursion Forests in the Sequential Screening Process: Consensus Selection by Multiple Recursion Trees. J. Chem. Inf. Comput. Sci. 2003, 43, 941-948.
24. Godden, J. W.; Furr, J. R.; Bajorath, J. Recursive Median Partitioning for Virtual Screening of Large Databases. J. Chem. Inf. Comput. Sci. 2003, 45, 182-188.
25. Miller, D. W. Results of a New Classification Algorithm Combining K Nearest Neighbors and Recursive Partitioning. J. Chem. Inf. Comput. Sci. 2001, 41, 168-175.
26. Blower, P.; Fligner, M.; Verducci, J.; Bjoraker, J. On Combining Recursive Partitioning and Simulated Annealing to Detect Groups of Biologically Active Compounds. J. Chem. Inf. Comput. Sci. 2002, 42, 393-404.
27. Cho, S. J.; Shen, C. F.; Hermsmeier, M. A. Binary Formal Inference-Based Recursive Modeling using Multiple Atom and Physicochemical Property Class Pair and Torsion Descriptors as Decision Criteria. J. Chem. Inf. Comput. Sci. 2000, 40, 668-680.
28. DeLisle, R. K.; Dixon, S. L. Induction of Decision Trees via Evolutionary Programming. J. Chem. Inf. Comput. Sci. 2004, 44, 862-870.
29. Chen, X.; Rusinko III, A.; Tropsha, A.; Young, S. S. Automated Pharmacophore Identification for Large Chemical Data Sets. J. Chem. Inf. Comput. Sci. 1999, 39, 887-896.
30. Wagener, M.; van Geerestein, V. J. Potential Drugs and Nondrugs: Prediction and Identification of Important Structural Features. J. Chem. Inf. Comput. Sci. 2000, 40, 280-292.
31. Childers, W. E., Jr.; Abou-Gharbia, M. A.; Kelly, M. G.; Andree, T. H.; Harrison, B. L.; Ho, D. M.; Hornby, G.; Huryn, D. M.; Rosenzweig-Lipson, S. J.; Schmid, J.; Smith, D. L.; Sukoff, S. J.; Zhang, G.; Schechter, L. J. Med. Chem. 2005, 48, 3467-3470.
32. Turner, D. B.; Tyrrell, S. M.; Willett, P. Rapid Quantification of Molecular Diversity for Selective Database Acquisition. J. Chem. Inf. Comput. Sci. 1997, 37, 18-22.