Global mapping of pharmacological space

Nature Biotechnology

Volumn 24, Issue 7, 2006, Pages 805-815

  Paolini, Gaia V ^a   Shapland, Richard H B ^a,b   Van Hoorn, Willem P ^a   Mason, Jonathan S ^a,c   Hopkins, Andrew L ^a  

^a PFIZER GLOBAL RESEARCH AND DEVELOPMENT   (United Kingdom)

^b Servefile Software Ltd   (United Kingdom)

^c H LUNDBECK A S   (Denmark)

Author keywords

[No Author keywords available]

Indexed keywords

DISEASES; ENGINEERING RESEARCH; MATHEMATICAL MODELS; PRODUCTIVITY; PROTEINS; STRUCTURE (COMPOSITION);

CHEMICAL TOOLS; PHARMACOLOGICAL SPACE; POLYPHARMACOLOGY; STRUCTURE ACTIVITY RELATIONSHIPS (SAR);

DRUG PRODUCTS;

ALPHA 1A ADRENERGIC RECEPTOR; ALPHA 2B ADRENERGIC RECEPTOR; ASPARTIC PROTEINASE; CYSTEINE PROTEINASE; CYTOCHROME P450 2C19; CYTOCHROME P450 2D6; CYTOCHROME P450 3A4; CYTOCHROME P450 3A5; DOPAMINE 2 RECEPTOR; ENZYME; HISTAMINE H1 RECEPTOR; HISTAMINE H2 RECEPTOR; HORMONE RECEPTOR; HYDROLASE; IMIDAZOLINE I1 RECEPTOR; ION CHANNEL; METALLOPROTEINASE; MUSCARINIC RECEPTOR; OXIDOREDUCTASE; PEPTIDE; PHOSPHODIESTERASE; PHOSPHOTRANSFERASE; PROTEIN; PROTEIN KINASE; PROTEIN KINASE C DELTA; SERINE PROTEINASE; SEROTONIN 1A RECEPTOR; SEROTONIN 2 RECEPTOR; SEROTONIN 7 RECEPTOR; TRANSFERASE;

AMINO ACID SEQUENCE; ARTICLE; AUTOMATION; BAYES THEOREM; BINDING AFFINITY; BIOINFORMATICS; BIOLOGICAL ACTIVITY; CALCULATION; CHEMICAL STRUCTURE; CHEMISTRY; DATA BASE; DRUG ABSORPTION; DRUG PROTEIN BINDING; DRUG SCREENING; DRUG TARGETING; GENOMICS; HUMAN GENOME; MULTIGENE FAMILY; PREDICTION; PRIORITY JOURNAL; PROTEIN STRUCTURE; STATISTICAL SIGNIFICANCE; STRUCTURE ACTIVITY RELATION; TAXONOMY;

COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; DATABASES, FACTUAL; DATABASES, PROTEIN; HUMANS; MODELS, CHEMICAL; MODELS, STRUCTURAL; PHARMACEUTICAL PREPARATIONS; PHARMACOPOEIAS; STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 33746156959     PISSN: 10870156     EISSN: 15461696     Source Type: Journal    
DOI: 10.1038/nbt1228     Document Type: Article

References (52)

1. Schuffenhauer, A. & Jacoby, E. Annotating and mining the ligand-target chemogenomics knowledge space. Drug Discov. Today: BIOSILICO 2, 190-200 (2004).
2. Strausberg, R.L. & Schreiber, S.L. From knowing to controlling: a path from genomics to drugs using small molecule probes. Science 300, 294-295 (2003).
3. Weinstein, J.N. et al. An information intensive approach to the molecular pharmacology of cancer. Science 275, 343-349 (1997).
4. Roth, B.L., Kroeze, W.K., Patel, S. & Lopez, E. The multiplicity of serotonin receptors: uselessly diverse molecules or an embarrasment of riches? Neuroscientist 6, 252-262 (2000).
5. Krejsa, C.M. et al. Predicting ADME properties and side effects: the BioPrint approach. Curr. Opin. Drug Discov. Develop. 6, 470-480 (2003).
6. 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, 680-690 (2003).
7. Root, D.E., Flaherty, S.P., Kelley, B.P. & Stockwell, B. Biological mechanism profiling using an annotated compound library. Chem. Biol. 10, 881-892 (2003).
8. Wallqvist, A. et al. Mining the NCI screening database: explorations of agents involved in cell cycle regulation. Prog. Cell Cycle Res. 5, 173-179 (2003).
9. Piatetski-Shapiro, G. & Frawley, W. Knowledge Discovery in Databases (MIT Press, Cambridge, 1992).
10. Klösgen, W. & Zytkow, J.M. (eds.). Handbook of Data Mining and Knowledge Discovery (Oxford University Press, Oxford, 2002).
11. Drews, J. Genomic sciences and the medicine of tomorrow. Nat. Biotechnol. 14, 1516-1518 (1996).
12. Drews, J. & Ryser, S. Classic drug targets. Nat. Biotechnol. 15, 1318-1319 (1997).
13. Hopkins, A.L. & Groom, C.R. The druggable genome. Nat. Rev. Drug Discov. 1, 727-730 (2002).
14. Golden, J.B. Prioritizing the human genome: knowledge management for drug discovery. Curr. Opin. Drug Discov. Develop. 6, 310-316 (2003).
15. Lipinski, C.A., Lombardo, F., Dominy, B.W. & Feeney, P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Del. Rev. 23, 3-25 (1997).
16. Van Gestel, S. & Schuermans, V. Thirty-three years of drug discovery and research with Dr. Paul Janssen. Drug Dev. Res. 8, 1-13 (1986).
17. Sneader, W. Drug Prototypes and Their Exploitation (Wiley, London, 1996).
18. Wermuth, C.G. Selective optimization of side activities: another way for drug discovery. J. Med. Chem. 47, 1303-1314 (2004).
19. McGovern, S.L., Helfand, B.T., Feng, B. & Shoichet, B.K. A specific mechanism of nonspecific inhibition. J. Med. Chem. 46, 4265-4272 (2003).
20. Vieth, M. et al. Kinomics - structural biology and chemogenomics of kinase inhibitors and targets. Biochim. Biophys. Acta 1697, 243-257 (2004).
21. Vieth, M., Sutherland, J.J., Robertson, D.H. & Campbell, R.M. Kinomics: characterizing the therapeutically validated kinase space. Drug Discov. Today 10, 839-846 (2005).
22. Frye, S.V. Structure-activity relationship homology (SARAH): a conceptual framework for drug discovery in the genomic era. Chem. Biol. 6, R3-R7 (1999).
23. Xia, X., Maliski, E.G., Gallant, P. & Rogers, D. Classification of kinase inhibitors using a Bayesian model. J. Med. Chem. 47, 4463-4470 (2004).
24. Rogers, D., Brown, R.D. & Hahn, M. Using extended-connectivity fingerprints with laplacian-modified Bayesian analysis in high-throughput screening follow-up. J. Biomol. Screen. 10, 682-686 (2005).
25. Lipinski, C. & Hopkins, A. Navigating chemical space for biology and medicine. Nature 432, 855-861 (2004).
26. Vieth, M. et al. Characteristic physical properties and structural fragments of marketed oral drugs. J. Med. Chem. 47, 224-232 (2004).
27. Wenlock, M.C., Austin, R.P., Barton, P., Davis, A.M. & Leeson, P.D. A comparison of physiochemical property profiles of development and marketed oral drugs. J. Med. Chem. 46, 1250-1256 (2003).
28. Blake, J.F. Examination of the computed molecular properties of compounds selected for clinical development. Biotechniques (June) Suppl., 16-20 (2003).
29. Ajay, A., Walters, W.P. & Murcko, M.A. Can we learn to distinguish between "drug-like" and "nondrug-like" molecules? J. Med. Chem. 41, 3314-3324 (1998).
30. Lipinski, C.A. Drug-like properties and the causes of poor solubility and poor permeability. J. Pharmacol. Toxicol. Methods 44, 235-249 (2000).
31. Wang, J. & Ramnarayan, K. Towards designing drug-like libraries: a novel computational approach for prediction of drug feasibility of compounds. J. Comb. Chem. 1, 524-533 (1999).
32. Walters, W.P. Ajay & Murcko, M.A. Recognizing molecules with drug-like properties. Curr. Opin. Chem. Biol. 3, 384-387 (1999).
33. Podlogar, B.L., Muegge, I. & Brice, L.J. Computational methods to estimate drug development paramenters. Curr. Opin. Drug Discov. Devel. 4, 102-109 (2001).
34. Muegge, I., Heald, S.L. & Brittelli, D. Simple selection criteria for drug-like chemical matter. J. Med. Chem. 44, 1841-1846 (2001).
35. Veber, D.F. et al. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem. 45, 2615-2623 (2002).
36. Proudfoot, J.R. Drugs, leads, and drug-likeness: an analysis of some recently launched drugs. Bioorg. Med. Chem. Lett. 12, 1647-1650 (2002).
37. Egan, W.J., Walters, W.P. & Murcko, M.A. Guiding molecules towards drug-likeness. Curr. Opin. Drug Discov. Devel. 5, 540-549 (2002).
38. Walters, W.P. & Murcko, M.A. Prediction of 'drug-likeness'. Adv. Drug Deliv. Rev. 54, 255-271 (2002).
39. Muegge, I. Selection criteria for drug-like compounds. Med. Res. Rev. 23, 302-321 (2003).
40. Lajiness, M.S., Vieth, M. & Erickson, J. Molecular properties that influence oral drug-like behavior. Curr. Opin. Drug Discov. Devel. 7, 470-477 (2004).
41. Stockwell, B.R. Chemical genetics: ligand-based discovery of gene function. Nat. Rev. Genet. 1, 116-125 (2000).
42. Austin, C.P., Brady, L.S., Insel, T.R. & Collins, F.S. NIH Molecular Libraries Initiative. Science 306, 1138-1139 (2004).
43. Schuffenhauer, A. et al. An ontology for pharmaceutical ligands and its applications for in silico screening and library design. J. Chem. Inf. Comput. Sci. 42, 947-955 (2002).
44. Feldman, H.J., Dumontier, M., Ling, S., Haider, N. & Hogue, C.W. CO: A chemical ontology for identification of functional groups and semantic comparison of small molecules. FEBS Lett. 579, 4685-4691 (2005).
45. Roth, B.L., Sheffler, D.J. & Kroeze, W.K. Magic shotguns versus magic bullets: selectively non-selective drugs for mood disorders and schizophrenia. Nat. Rev. Drug Discov. 3, 353-359 (2004).
46. Frantz, S. Drug discovery: playing dirty. Nature 437, 942-943 (2005).
47. Connolly, T. & Begg, C. Database Systems, A Practical Approach to Design, Implementation and Management., edn. 3 (Addison Wesley, Reading, MA, 2002).
48. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498-2504 (2003).
49. R Core Development Team. A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2005).
50. Andrews, P.R., Craik, D.J. & Martin, J.L. Functional group contributions to drug-receptor interactions. J. Med. Chem. 27, 1648-1657 (1984).
51. Hopkins, A.L., Groom, C.R. & Alex, A. Ligand efficiency: a useful metric for lead selection. Drug Discov. Today 9, 430-431 (2004).
52. Kuntz, I.D., Chen, K., Sharp, K.A. & Kollman, P.A. The maximal affinity of ligands. Proc. Natl. Acad. Sci. USA 96, 9997-10002 (1999).