Knowledge-based chemoinformatic approaches to drug discovery

Drug Discovery Today

Volumn 11, Issue 23-24, 2006, Pages 1107-1114

  Ghose, Arup K ^a   Herbertz, Torsten ^a   Salvino, Joseph M ^a   Mallamo, John P ^a  

^a Cephalon ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANTIDEPRESSANT AGENT; ANTIFUNGAL AGENT; ANTIINFECTIVE AGENT; ANTINEOPLASTIC AGENT; CARDIOVASCULAR AGENT; CELL NUCLEUS RECEPTOR; CENTRAL NERVOUS SYSTEM AGENTS; FUNCTIONAL GROUP; G PROTEIN COUPLED RECEPTOR; HYPNOTIC AGENT; INTEGRIN RECEPTOR; LIGAND; NEUROLEPTIC AGENT; PHOSPHOTRANSFERASE; PHOSPHOTRANSFERASE INHIBITOR; PROTEIN INHIBITOR;

BLOOD BRAIN BARRIER; COMPUTER PROGRAM; DRUG INDUSTRY; DRUG SCREENING; DRUG STRUCTURE; KNOWLEDGE BASE; MATHEMATICAL MODEL; PHARMACOPHORE; PHYSICAL CHEMISTRY; PROTEIN TARGETING; REFERENCE DATABASE; REVIEW; SCIENTIST; STRUCTURE ANALYSIS;

DRUG DESIGN; DRUG INDUSTRY; HUMANS; KNOWLEDGE BASES; MEDICAL INFORMATICS; MOLECULAR STRUCTURE; PHARMACEUTICAL PREPARATIONS;

EID: 33751313329     PISSN: 13596446     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.drudis.2006.10.012     Document Type: Review

References (50)

1. Pharmaceutical Research and Manufacturers of America, Pharmaceutical Industry Profile 2005 (Washington, DC, March)
2. Bemis G.W., and Murcko M.A. Properties of known drugs. I. Molecular frameworks. J. Med. Chem. 37 (1996) 2887-2893
3. McGregor M.J., and Pallai P.V. Clustering large databases of compounds using MDL "Keys" as structural descriptors. J. Chem. Inf. Comput. Sci. 37 (1997) 443-448
4. Lipinski C.A., et al. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 23 (1997) 3-25
5. Ajay, et al. Can we learn to distinguish between 'drug-like' and 'nondrug-like' molecules?. J. Med. Chem. 41 (1998) 3314-3324
6. Sadowski J., and Kubinyi H. Atom-type based neural net model of drug-likeness. J. Med. Chem. 41 (1998) 3325-3329
7. In: Hansch C., et al. (Ed). Comprehensive Medicinal Chemistry (1990), Pergamon Press, Oxford
8. Ghose A.K., et al. A knowledge-based approach in designing combinatorial and medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drugs databases. J. Comb. Chem. 1 (1999) 55-68
9. Lajiness M.S., et al. Molecular properties that influence oral drug-like behavior. Curr. Opin. Drug Discov. Devel. 7 (2004) 470-477
10. Wenlock M.C., et al. A comparison of physicochemical property profiles of development and marketed oral drugs. J. Med. Chem. 46 (2003) 1250-1256
11. Leeson P.D., and Davis A.M. Time-related differences in the physical property profiles of oral drugs. J. Med. Chem. 47 (2004) 6338-6348
12. Baurin N., et al. Drug-like annotation and duplicate analysis of a 23-supplier chemical database totaling 2.7 million compounds. J. Chem. Inf. Comput. Sci. 44 (2004) 643-651
13. Ghose A.K., et al. Prediction of hydrophobic properties of small organic molecules using fragmental methods: an analysis of AlogP and ClogP methods. J. Phys. Chem. 102 (1998) 3762-3772
14. Lipinski, C.A. (2005) Filtering in drug discovery. In Annual reports in computational chemistry (Vol. 1) (Spellmeyer. D. C., Ed.), pp. 155-168, Elsevier
15. Morphy R. The influence of target family and functional activity on the physicochemical properties of preclinical compounds. J. Med. Chem. 49 (2006) 2969-2978
16. Pirard B., and Pickett S.D. Classification of kinase inhibitors using BCUT descriptors. J. Chem. Inf. Comput. Sci. 40 (2000) 1431-1440
17. Palm K., et al. Polar molecular surface properties predict the intestinal absorption of drugs in human. Pharm. Res. 14 (1997) 568-571
18. Kelder J., et al. Polar molecular surface as a dominating determinant for oral absorption and brain penetration of drugs. Pharm. Res. 16 (1999) 1514-1519
19. Ertl P., et al. Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. J. Med. Chem. 43 (2000) 3714-3717
20. Egan W.J., et al. Prediction of drug absorption using multivariate statistics. J. Med. Chem. 43 (2000) 3867-3877
21. th ACS national meeting, San Francisco, CA, March 26-30, CINF-002. American Chemical Society, Washington, D.C.
22. Vieth M., et al. Characteristic physical properties and structural fragments of marketed oral drugs. J. Med. Chem. 47 (2004) 224-232
23. Manallack D.T., et al. Selecting screening candidates for kinase and G protein-coupled receptor targets using neural networks. J. Chem. Inf. Comput. Sci. 42 (2002) 1256-1262
24. Gund P. Evolution of pharmacophore concept in pharmaceutical research. In: Guner O.F. (Ed). Pharmacophore perception, development and the use in drug design (2000), IUL-press, La Jolla 3-12
25. Ghose A.K., and Wendoloski J.J. Pharmacophore modelling: methods, experimental verification and applications. Perspect. Drug Discov. And Des. 9 (1998) 253-271
26. Ghose A.K., et al. Determination of pharmacophoric geometry for collagenase inhibitors using a novel computational method and its verification using molecular dynamics, NMR and X-ray crystallography. J. Am. Chem. Soc. 117 (1995) 4671-4682
27. Takeuchi Y., et al. Derivation of three-dimensional pharmacophores model of substance P antagonists bound to the neurokinin-1 receptor. J. Med. Chem. 41 (1998) 3609-3623
28. In: Johnson A.M., and Maggiora G.M. (Eds). Concepts and applications of molecular similarity (1990), Wiley, New York
29. Willett P., et al. Chemical similarity searching. J. Chem. Inf. Comput. Sci. 38 (1998) 983-996
30. Patchett A.A., and Nargund R.P. Privileged structures - an update. In: Doherty A.M. (Ed). Annual reports in medicinal chemistry (Vol. 35) (2000), Elsevier 289-298
31. Tounge B.A., and Reynolds C.H. Defining privileged reagents using subsimilarity comparison. J. Chem. Inf. Comput. Sci. 44 (2004) 1810-1815
32. Klabunde T., and Hessler G. Drug design strategies for targeting G-protein-coupled receptors. ChemBioChem 3 (2002) 928-944
33. Prien O. Target-family-oriented focused libraries for kinases - conceptual design aspects and commercial availability. ChemBioChem 6 (2005) 500-505
34. Chen X., and Reynolds C.H. Performance of similarity measures in 2D fragment-based similarity searching: comparison of structural descriptors and similarity coefficients. J. Chem. Inf. Comput. Sci. 42 (2002) 1407-1414
35. Bender A., et al. Similarity searching of chemical databases using atom environment descriptors (MOLPRINT 2D): evaluation of performance. J. Chem. Inf. Comput. Sci. 44 (2004) 1708-1718
36. Martin Y.C., et al. Do structurally similar molecules have similar biological activity?. J. Med. Chem. 45 (2002) 4350-4358
37. Ghose A.K., et al. Structural mimicry of adenosine by the antitumor agents 4-methoxy- and 4-amino-8-(β-D-ribofuranosylamino)pyrimido[5,4-d]pyrimidine as viewed by a molecular modeling method. Proc. Natl. Acad. Sci. U. S. A. 86 (1989) 8242-8246
38. Itai A., et al. A receptor model for tumor promoters: rational superposition of teleocidins and phorbol esters. Proc. Natl. Acad. Sci. U. S. A. 85 (1988) 3688-3692
39. Wildman S.A., and Crippen G.M. Evaluation of ligand overlap by atomic parameters. J. Chem. Inf. Comput. Sci. 41 (2001) 446-450
40. Xu J. GMA: a generic match algorithm for structural homomorphism, isomorphism and maximal common substructure match and its application. J. Chem. Inf. Comput. Sci. 36 (1996) 25-34
41. Raymond J.W., and Willett P. Maximum common subgraph isomorphism algorithms for the matching of chemical structures. J. Comput.-Aided Mol. Design 16 (2002) 521-533
42. Stahl M., et al. A robust clustering method for chemical structures. J. Med. Chem. 48 (2005) 4358-4366
43. Schnur D.M., et al. Are target-family privileged substructures truly privileged?. J. Med. Chem. 49 (2006) 2000-2009
44. Sheridan R.P. The most common chemical replacements in drug-like compounds. J. Chem. Inf. Comput. Sci. 42 (2002) 103-108
45. Rishton G.M. Reactive compounds and in vitro false positives in HTS. Drug Discov. Today 2 (1997) 384-386
46. Pearce B.C., et al. An empirical process for the design of high-throughput-screening deck filters. J. Chem. Inf. Model 46 (2006) 1060-1068
47. Lajiness M.S., et al. Assessment of the consistency of medicinal chemists in reviewing set of compounds. J. Med. Chem. 47 (2004) 4891-4896
48. 13C NMR approach to categorizing potential limitations of α,β-unsaturated carbonyl systems in drug-like molecules. Bioorg. Med. Chem. Lett. 14 (2004) 5503-5507
49. Feng B.Y., and Shoichet B.K. A detergent-based assay for the detection of promiscuous inhibitors from virtual and high-throughput screening. Nature Protocols 1 (2006) 550-553
50. Lombardo F., et al. In silico ADME prediction: data, models, facts and myths. Mini Reviews Med. Chem. 3 (2003) 861-875