Annotating and mining the ligand-target chemogenomics knowledge space

Drug Discovery Today: BIOSILICO

Volumn 2, Issue 5, 2004, Pages 190-200

  Schuffenhauer, Ansgar ^a   Jacoby, Edgar ^a  

^a NOVARTIS PHARMA AG   (Switzerland)

Author keywords

Drug Discovery; lead finding; library design; molecular informatics; Pharmaceutical Science; pharmacoinformatics

Indexed keywords

LIGAND; SEROTONIN 7 RECEPTOR; SEROTONIN ANTAGONIST;

DATA BASE; DRUG SCREENING; DRUG TARGETING; INFORMATION SCIENCE; PHARMACOPHORE; REVIEW;

EID: 5344268130     PISSN: 17418364     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1741-8364(04)02408-4     Document Type: Review

References (60)

1. Frye S.V. Structure-activity relationship homology (SARAH): a conceptual framework for drug discovery in the genomic era. Chem. Biol. 6:1999;R3-R7
2. Jacoby E., et al. Chemogenomics knowledge-based strategies in drug discovery. Drug News Perspect. 16:2003;93-102
3. Bleicher K.H., et al. A guide to drug discovery: hit and lead generation: beyond high-throughput screening. Nat. Rev. Drug Discov. 2:2003;369-378
4. Berman H.M., et al. The protein data bank. Nucleic Acids Res. 28:2000;235-242
5. Berman H.M., et al. The protein data bank. Acta Crystallogr. D. Biol. Crystallogr. 58:2002;899-907
6. Hendlich M., et al. Databases for protein-ligand complexes. Acta Crystallogr. D. Biol. Crystallogr. 54:1998;1178-1182
7. Boeckmann B., et al. The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res. 31:2003;365-370
8. Wheeler D.L., et al. Genbank. Nucleic Acids Res. 31:2003;23-27
9. Strausberg R.L., Schreiber S.L. From knowing to controlling: a path from genomics to drugs using small molecule probes. Science. 300:2003;294-295
10. Roth B.L., et al. The multiplicity of serotonin receptors: uselessly diverse molecules or an embarrassment of riches? Neuroscientist. 6:2000;252-262
11. Stockwell B.R., et al. Biological mechanism profiling using an annotated compound library. Chem. Biol. 10:2003;881-892
12. Weininger D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28:1988;31-36
13. Ash S., et al. SYBYL line notation (SLN): a versatile language for chemical structure representation. J. Chem. Inf. Comput. Sci. 37:1997;71-79
14. Morgan H.L. The generation of a unique machine description for chemical structure-A technique developed at chemical abstracts service. J. Chem. Doc. 5:1965;107-113
15. Stein, S.E. et al (2003) Open standards for chemical information: The IUPAC chemical identifier and data dictionary projects. Abstracts of papers, 226th ACS National Meeting, New York, NY, United States, September 7-11, 2003
16. Balakin K.V., et al. Advanced exact structure searching in large databases of chemical compounds. J. Chem. Inf. Comput. Sci. 43:2003;852-860
17. Sayle, R., Skillman, G. (2002) Hooked on protonics. Abstracts of papers, 224th ACS National Meeting, Boston, MA, United States, 18-22 August, 2002
18. The Gene Ontology Consortium. Gene ontology: tool for the unification of biology. Nat. Genet. 25:2000;25-29
19. Horn F., et al. GPCRDB: an information system for G protein-coupled receptors. Nucleic Acids Res. 26:1998;275-279
20. Horn F., et al. Collecting and harvesting biological data: the GPCRDB and NuclearDB information systems. Nucleic Acids Res. 29:2001;346-349
21. The Nomenclature Committee of the International Union of Biochemistry and Molecular Biology, Enzyme Nomenclature (1992), Academic Press, ISBN 0-12-227164-5; Supplement 1: Eur. J. Biochem. 1994, 223, 1-5; Supplement 2: Eur. J. Biochem. 1995, 232, 1-6; Supplement 3: Eur. J. Biochem. 1996, 237, 1-5; Supplement 4: Eur. J. Biochem. 1997, 250; 1-6; Supplement 5. Eur. J. Biochem. 264, 610-650
22. The Nomenclature Committee of the International Union of Biochemistry and Molecular Biology, Enzyme Nomenclature (1992), Academic Press, ISBN 0-12-227164-5; Supplement 1: Eur. J. Biochem. 1994, 223, 1-5; Supplement 2: Eur. J. Biochem. 1995, 232, 1-6; Supplement 3: Eur. J. Biochem. 1996, 237, 1-5; Supplement 4: Eur. J. Biochem. 1997, 250; 1-6; Supplement 5. Eur. J. Biochem. 264, 610-650
23. The Nomenclature Committee of the International Union of Biochemistry and Molecular Biology, Enzyme Nomenclature (1992), Academic Press, ISBN 0-12-227164-5; Supplement 1: Eur. J. Biochem. 1994, 223, 1-5; Supplement 2: Eur. J. Biochem. 1995, 232, 1-6; Supplement 3: Eur. J. Biochem. 1996, 237, 1-5; Supplement 4: Eur. J. Biochem. 1997, 250; 1-6; Supplement 5. Eur. J. Biochem. 264, 610-650
24. The Nomenclature Committee of the International Union of Biochemistry and Molecular Biology, Enzyme Nomenclature (1992), Academic Press, ISBN 0-12-227164-5; Supplement 1: Eur. J. Biochem. 1994, 223, 1-5; Supplement 2: Eur. J. Biochem. 1995, 232, 1-6; Supplement 3: Eur. J. Biochem. 1996, 237, 1-5; Supplement 4: Eur. J. Biochem. 1997, 250; 1-6; Supplement 5. Eur. J. Biochem. 264, 610-650
25. The Nomenclature Committee of the International Union of Biochemistry and Molecular Biology, Enzyme Nomenclature (1992), Academic Press, ISBN 0-12-227164-5; Supplement 1: Eur. J. Biochem. 1994, 223, 1-5; Supplement 2: Eur. J. Biochem. 1995, 232, 1-6; Supplement 3: Eur. J. Biochem. 1996, 237, 1-5; Supplement 4: Eur. J. Biochem. 1997, 250; 1-6; Supplement 5. Eur. J. Biochem. 264, 610-650
26. Schuffenhauer A., et al. An ontology for pharmaceutical ligands and its application for in silico screening and library design. J. Chem. Inf. Comput. Sci. 42:2002;947-955
27. Orengo C.A., et al. The CATH protein family database: A resource for structural and functional annotation of genomes. Proteomics. 2:2002;11-21
28. Orengo C.A., et al. The CATH database: an extended protein family resource for substructural and functional genomics. Nucleic Acids Res. 31:2003;452-455
29. Murzin A.G., et al. SCOP: a structural classification of proteins database for the investigation of sequences and structures. J. Mol. Biol. 247:1995;536-540
30. Blower P.E., et al. LeadScope: software for exploring large sets of screening data. J. Chem. Inf. Comput. Sci. 40:2000;1302-1314
31. Willett P., et al. Atom-by-atom searching using massive parallelism. Implementation of the Ullmann subgraph isomorphism algorithm on the distributed array processor. J. Chem. Inf. Comput. Sci. 31:1991;225-233
32. Downs G.M., Willett P. Similarity searching in databases of chemical structures. Rev. Comp. Chem. 7:1996;1-66
33. Willett P., et al. Chemical similarity searching. J. Chem. Inf. Comput. Sci. 38:1998;157-166
34. Holliday J.D., et al. Analysis and display of the size dependence of chemical similarity coefficients. J. Chem. Inf. Comput. Sci. 43:2003;819-828
35. Good A.C., et al. Investigating the extension of pairwise distance pharmacophore measures to triplet-based descriptors. J. Comput. Aided Mol. Des. 9:1995;373-379
36. Pickett D.S., et al. Diversity profiling and design using 3D-pharmacophores: pharmacophore-derived queries (PDQ). J. Chem. Inf. Comput. Sci. 36:1996;1214-1223
37. Matter H. Selecting optimally diverse compounds from structure databases: A validation study of two-dimensional and three-dimensional molecular descriptors. J. Med. Chem. 40:1997;1219-1229
38. Mason J.S., et al. New 4-point pharmacophore method for molecular similarity and diversity applications: Overview of the method and applications, including a novel approach to the design of combinatorial libraries containing privileged substructures. J. Med. Chem. 42:1999;3251-3264
39. Martin E.J., Hoeffel T.J. Oriented substituent pharmacophore property space (OSPPREYS): a substituent-based calculation that describes combinatorial library products better than the corresponding product-based calculation. J. Mol. Graph. Model. 18:2000;383-403
40. Makara G.M. Measuring molecular similarity and diversity: total pharmacophore diversity. J. Med. Chem. 44:2001;3563-3571
41. Horvath D. High throughput conformational sampling and fuzzy similarity metrics: a novel approach to similarity searching and focussed combinatorial library design and its role in the drug discovery laboratory. Ghose A., Viswanadhan V. Combinatorial Library Design and Evaluation: Principle, Software Tools and Applications. 2001;429-472 Marcel Dekker, New York
42. Schuffenhauer A., et al. Similarity metrics for ligands reflecting the similarity of the target proteins. J. Chem. Inf. Comput. Sci. 43:2003;391-405
43. Schneider G., et al. Scaffold-hopping by topological pharmacophore search: A contribution to virtual screening. Angew. Chem. Int. Ed. Engl. 38:1999;2894-2896
44. Raymond J., et al. RASCAL: calculation of graph similarity using maximum common edge subgraphs. Comput. J. 45:2002;631-644
45. Raymond J.W., et al. Heuristics for similarity searching of chemical graphs using a maximum common edge subgraph algorithm. J. Chem. Inf. Comput. Sci. 42:2002;305-316
46. Altschul S.F. Basic local alignment search tool. J. Mol. Biol. 215:1990;403-410
47. Altschul S.F., et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25:1997;3389-3402
48. Thornton J.M., et al. From protein structure to biochemical function? J. Struct. Funct. Genomics. 4:2003;167-177
49. Schmitt S., et al. A new method to detect related function among proteins independent of sequence and fold homology. J. Mol. Biol. 323:2002;387-406
50. Downs G.M., Barnard J.M. Clustering methods and their uses in computational chemistry. Rev. Comp. Chem. 18:2002;1-40
51. MacCuish J., et al. Ties in proximity and clustering compounds. J. Chem. Inf. Comput. Sci. 41:2001;134-146
52. Raymond J.W., et al. Comparison of chemical clustering methods using graph- and fingerprint-based similarity measures. J. Mol. Graph. Model. 21:2003;421-433
53. Hayward J., Buchan I. Benefits of data cartridge technology for handling chemical information. Curr. Opin. Drug Discov. Dev. 3:2000;306-309
54. Sheridan R.P., Kearsley S.K. Why do we need so many chemical similarity search methods? Drug Discov. Today. 7:2002;903-911
55. Schneider P., Schneider G. Collection of bioactive reference compounds for focused library design. QSAR Comb. Sci. 22:2003;713-718
56. Weinstein, et al. An information intensive approach to the molecular pharmacology of cancer. Science. 275:1997;343-349
57. Horvath D., Jeandenans C. Neighborhood behavior of in silico structural spaces with respect to in vitro activity spaces - A novel understanding of the molecular similarity principle in the context of multiple receptor binding profiles. J. Chem. Inf. Comput. Sci. 43:2003;680-690
58. Chan A.W., et al. Protein binding pocket chemogenomics: how bioinformatics tools can help. PharmaGenomics. 2:2002;32-40
59. Michalovich D., et al. Protein sequence analysis in silico: application of structure-based bioinformatics to genomic initiatives. Curr. Opin. Pharmacol. 2:2002;574-580
60. Weir M., et al. Insights into protein function through large-scale computational analysis of sequence and structure. Trends Biotechnol. 19:2001;S61-S66