Hit finding: towards 'smarter' approaches

Current Opinion in Pharmacology

Volumn 9, Issue 5, 2009, Pages 589-593

  Langer, Thierry ^a   Hoffmann, Rémy ^b   Bryant, Sharon ^c   Lesur, Brigitte ^d  

^a Prestwick Chemical Inc   (France)

^b Parc Club Orsay Universite   (France)

^c Inte:Ligand GmbH   (Austria)

^d INSTITUT DE RECHERCHES SERVIER   (France)

Author keywords

[No Author keywords available]

Indexed keywords

DRUG;

ALGORITHM; ALPHA HELIX; ANALYTIC METHOD; BAYES THEOREM; COMPUTER MODEL; DATA BASE; DRUG DESIGN; DRUG INFORMATION; DRUG RESEARCH; DRUG SCREENING; HIGH THROUGHPUT SCREENING; LIGAND BINDING; MOLECULAR DOCKING; PHARMACOPHORE; PHYSICAL CHEMISTRY; PRIORITY JOURNAL; PROCESS OPTIMIZATION; PROTEIN TARGETING; QUALITY CONTROL; RELIABILITY; REVIEW; STRUCTURAL BIOINFORMATICS; STRUCTURE ACTIVITY RELATION; STRUCTURE ANALYSIS;

ANIMALS; COMPUTER SIMULATION; COMPUTER-AIDED DESIGN; DATABASES AS TOPIC; DRUG DESIGN; DRUG DISCOVERY; HIGH-THROUGHPUT SCREENING ASSAYS; HUMANS; LIGANDS; MODELS, MOLECULAR; SMALL MOLECULE LIBRARIES; STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 70349433676     PISSN: 14714892     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.coph.2009.06.001     Document Type: Review

References (51)

1. Waszkowycz B. Towards improving compound selection in structure-based virtual screening. Drug Discov Today 13 (2008) 219-226. This article highlight some of the technical challenges and discuss recent progress in structure based virtual screening.
2. Hristozov D.P., Oprea T.I., and Gasteiger J. Virtual screening applications: a study of ligand-based methods and different structure representations in four different scenarios. J Comput Aided Mol Des 21 (2007) 617-640
3. McGaughey J., Sheridan R.P., Bayly C.I., Culberson J.C., Kreatsoulas C., Lindsley S., Maiorov V., Truchon J.F., and Cornell W.D. Comparison of topological, shape, and docking methods in virtual screening. J Chem Inf Model 47 (2007) 1504-1519. This article is a large Benchmark study including comparison between 2D and 3D ligand similarity and protein structure based docking approaches.
4. Jacoby E., Schuffenhauer A., Popov M., Azzaoui K., Havill B., Schopfer U., Engeloch C., Stanek J., Acklin P., Rigollier P., et al. Key aspects of the Novartis compound collection enhancement project for the compilation of a comprehensive chemogenomics drug discovery screening collection. Curr Top Med Chem 5 (2005) 397-411. In this article authors assemble and review key aspects of the Novartis compound collection molecular diversity as well as compound management process.
5. Barnard J.M., Downs G.M., Scholley-Pfab A., and Brown R.D. Use of Markush structure analysis techniques for descriptor generation and clustering of large combinatorial libraries. J Mol Graph Model 18 (2000) 452-463
6. Fink T., and Reymond J.L. Virtual exploration of the chemical universe up to 11 atoms of C, N, O, F: assembly of 26.4 million structures (110.9 million stereoisomers) and analysis for new ring systems, stereochemistry, physicochemical properties, compound classes, and drug discovery. J Chem Inf Model 47 (2007) 342-353
7. Irwin J., and Shoichet B. ZINC-a free database of commercially available compounds for virtual screening. J Chem Inf Model 45 (2005) 177-182
8. Ghose A.K., Herbertz T., Salvino J.M., and Mallamo J.P. Knowledge-based chemoinformatic approaches to drug discovery. Drug Discov Today 11 (2006) 1107-1114. The authors present the recent trend in database of known biologically active ligands and give a good review of their use in the drug discovery process.
9. Walters W., Stahl M., and Murcko M. Virtual screening-an overview. Drug Discov Today 3 (1998) 160-178
10. Shoichet B.K. Virtual screening of chemical libraries. Nature 432 (2004) 862-865
11. Eckert H., and Bajorath J. Molecular similarity analysis in virtual screening: foundations, limitations and novel approaches. Drug Discov Today 12 (2007) 225-233. In this article, authors explain how different approaches of similarity analysis are of little overlap and the need of novel approaches to be developed for virtual screening.
12. Jarvis R., and Patrick E. Clustering using a similarity measure based on shared nearest neighbors IEEE. Trans Comput C22 (1973) 1025-1034
13. Rogers D., Brown R.D., and Hahn M. Using extended-connectivity fingerprints with Laplacian-modified Bayesian analysis in high-throughput screening follow-up. J Biomol Screen 10 (2005) 682-686
14. Durant J.L., Leland B.A., Henry D.R., and Nourse J.G. Reoptimization of MDL keys for use in drug discovery. J Chem Inf Comput Sci 42 (2002) 1273-1280
15. Godden J.W., Xue L., and Bajorath J. Combinatorial preferences affect molecular similarity/diversity calculations using binary fingerprints and Tanimoto coefficients. J Chem Inf Comput Sci 40 (2000) 163-166
16. Willett P. Similarity-based virtual screening using 2D fingerprints. Drug Discov Today 11 (2006) 1046-1053
17. Bernardo J, Smith AFM: Bayesian theory, 1994.
18. Klon A. Bayesian modeling in virtual high throughput screening. Comb Chem HTS 12 (2009) 469-483
19. Langer T., and Hoffmann R. In: Langer T., and Hoffmann R. (Eds). Pharmacophores and Pharmacophore Searches (2006), Wiley-VCH. That book cover in a clear and didactic manner all aspects of using pharmacophores approaches for Drug discovery.
20. Li H., Sutter J., and Hoffmann R. HypoGen: an automated system for generatin 3D predictive pharmacophore models. In: Gûner O.F. (Ed). Pharmacophore Perception, Development and Use in Drug Design (2000), International University Line 171-190
21. Dixon S.L., Smondyrev A.M., and Rao S.N. PHASE: a novel approach to pharmacophore modeling and 3D database searching. Chem Biol Drug Des 67 (2006) 370-372
22. Güner O.F., and Henry D.R. Metric for analyzing hit lists and pharamcophores. In: Gûner O.F. (Ed). Pharmacophore Perception, Development and Use in Drug Design (2000), International University Line 191-202
23. Triballeau N., Acher F., Brabet I., Pin J.P., and Bertrand H.O. Virtual screening workflow development guided by the 'receiver operating characteristic' curve approach Application to high-throughput docking on metabotropic glutamate receptor subtype 4. J Med Chem 48 (2005) 2534-2547
24. Blundell T.L., Jhoti H., and Abell C. High-throughput crystallography for lead discovery in drug design. Nat Rev Drug Discov 1 (2002) 45-54
25. Sutcliffe M.J., Haneef I., Carney D., and Blundell T.L. Knowledge based modelling of homologous proteins, Part I: Three-dimensional frameworks derived from the simultaneous superposition of multiple structures. Protein Eng 1 (1987) 377-384
26. Sutcliffe M.J., Hayes F.R., and Blundell T.L. Knowledge based modelling of homologous proteins. Part II: Rules for the conformations of substituted sidechains. Protein Eng 1 (1987) 385-392
27. Hanson M.A., Cherezov V., Griffith M.T., Roth C.B., Jaakola V.P., Chien E.Y., Velasquez J., Kuhn P., and Stevens R.C. A specific cholesterol binding site is established by the 2.8 A structure of the human beta2-adrenergic receptor. Structure 16 (2008) 897-905
28. Ortuso F., Langer T., and Alcaro S. GBPM: GRID-based pharmacophore model: concept and application studies to protein-protein recognition. Bioinformatics 22 (2006) 1449-1455
29. Wolber G., and Langer T. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J Chem Inf Model 45 (2005) 160-169
30. Lipinski C.A., Lombardo F., Dominy B.W., and Feeney P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 46 (2001) 3-26
31. Kellenberger E., Rodrigo J., Muller P., and Rognan D. Comparative evaluation of eight docking tools for docking and virtual screening accuracy. Proteins 57 (2004) 225-242
32. Warren G.L., Andrews C.W., Capelli A.M., Clarke B., LaLonde J., Lambert M.H., Lindvall M., Nevins N., Semus S.F., Senger S., et al. A critical assessment of docking programs and scoring functions. J Med Chem 49 (2006) 5912-5931
33. Sherman W., Beard H.S., and Farid R. Use of an induced fit receptor structure in virtual screening. Chem Biol Drug Des 67 (2006) 83-84
34. Feher M. Consensus scoring for protein-ligand interactions. Drug Discov Today 11 (2006) 421-428. This paper report how consensus scoring substantially improves target based virtual screening performance.
35. Seifert M. Targeted scoring functions for virtual screening. Drug Discov Today 14 (2009) 562-569
36. Moult J., Fidelis K., Kryshtafovych A., Rost B., Hubbard T., and Tramontano A. Critical assessment of methods of protein structure prediction-round VII. Proteins 69 Suppl. 8 (2007) 3-9
37. Oprea T.I. Current trends in lead discovery: are we looking for the appropriate properties?. Mol Divers 5 (2002) 199-208
38. Roche O., Schneider P., Zuegge J., Guba W., Kansy M., Alanine A., Bleicher K., Danel F., Gutknecht E.M., Rogers-Evans M., et al. Development of a virtual screening method for identification of 'frequent hitters' in compound libraries. J Med Chem 45 (2002) 137-142
39. Cummings M.D., Gibbs A.C., and DesJarlais R.L. Processing of small molecule databases for automated docking. Med Chem 3 (2007) 107-113
40. Gola J., Obrezanova O., Champness E., and Segall M. ADMET Property prediction: the state of the art and current challenges. QSAR Combi Sci 3 (2007) 1172-1180
41. Langer T., and Bryant S.D. In silico screening: hit finding from database mining. In: Wermuth C.G. (Ed). The Practice of Medicinal Chemistry (2008), Elsevier 210-227. A complete chapter that gives a good overview on Hit finding with more then 200 references to publications.
42. Morand K., and Cheng X. Organic compound stability in large, diverse pharmaceutical screening collections. In: Yan B., and Winefordner J.D. (Eds). Analysis and Purification Methods in Combinatorial Chemistry 163 (2004), John Wiley & Sons, Hoboken, NJ 321-350
43. Schuffenhauer A., Floersheim P., Acklin P., and Jacoby E. Similarity metrics for ligands reflecting the similarity of the target proteins. J Chem Inf Comput Sci 43 (2003) 391-405
44. Winkler D., and Burden F. Baysan neural nets for modeling in drug discovery. Drug Discov Today: BIOSILICO 2 (2004) 104-111
45. Schuster D., Laggner C., Steindl T.M., and Langer T. Development and validation of an in silico P450 profiler based on pharmacophore models. Curr Drug Discov Technol 3 (2006) 1-48
46. Howe T.J., Mahieu G., Marichal P., Tabruyn T., and Vugts P. Data reduction and representation in drug discovery. Drug Discov Today 12 (2007) 45-53. This article present new insights into data reduction, simplification and presentation and explain how it bring efficiency in data interpretation.
47. Wunberg T., Hendrix M., Hillisch A., Lobell M., Meier H., Schmeck C., Wild H., and Hinzen B. Improving the hit-to-lead process: data-driven assessment of drug-like and lead-like screening hits. Drug Discov Today 11 (2006) 175-180
48. Wermuth C. The SOSA approach: an alternative to high-throughput screening. Med Chem Res 10 (2001) 431-439
49. Wermuth C.G. Selective optimization of side activities: the SOSA approach. Drug Discov Today 11 (2006) 160-164. This article present how, starting from a set of carefully chosen structurally diverse known drug molecules, can reduce time and cost in drug discovery process.
50. Kumar K., and Waldmann H. Synthesis of natural product inspired compound collections. Angew Chem Int Ed Engl 48 (2009) 3224-3242
51. Lipinsky C. Chemistry quality and the medicinal chemistry-biology interface. Presentation at the 6th Winter Conference on Medicinal & Bioorganic Chemistry, Steamboat Springs, Colorado, January 23-29 (2005). http://www.mbcfoundation.org/pdfs/Lipinski%20Keynote%20address.pdf The presentation is available at http://www.mbcfoundation.org/pdfs/Lipinski Keynote address.pdf