High-throughput and in silico screenings in drug discovery

Expert Opinion on Drug Discovery

Volumn 4, Issue 9, 2009, Pages 947-959

  Phatak, Sharangdhar S ^a   Stephan, Clifford C ^a   Cavasotto, Claudio N ^a  

^a UNIVERSITY OF TEXAS   (United States)

Author keywords

Docking; Drug discovery; High throughput screening; In silico screening

Indexed keywords

COMPUTER MODEL; COST BENEFIT ANALYSIS; DRUG INDUSTRY; DRUG SCREENING; HIGH THROUGHPUT SCREENING; INFORMATION TECHNOLOGY; INTERMETHOD COMPARISON; PRIORITY JOURNAL; REVIEW; STRUCTURE ACTIVITY RELATION;

EID: 70249106425     PISSN: 17460441     EISSN: None     Source Type: Journal    
DOI: 10.1517/17460440903190961     Document Type: Review

References (118)

1. Macarron R. Critical review of the role of HTS in drug discovery. Drug Discov Today 2006;11(7-8):277-279
2. Drews J. Drug discovery: a historical perspective. Science 2000;287(5460):1960-1964
3. Mayr LM, Fuerst P. The future of high-throughput screening. J Biomol Screen 2008;13(6):443-448
4. Keseru GM, Makara GM. The influence of lead discovery strategies on the properties of drug candidates. Nat Rev Drug Discov 2009;8(3):203-212
5. Entzeroth M. Emerging trends in high-throughput screening. Curr Opin Pharmacol 2003;3(5):522-529
6. Keseru GM, Makara GM. Hit discovery and hit-to-lead approaches. Drug Discov Today 2006;11(15-16):741-748
7. Mahapatra A. Screening for success. ACS Chem Biol 2008;3(8):449-450
8. Harper G, Pickett SD, Green DV. Design of a compound screening collection for use in high throughput screening. Comb Chem High Throughput Screen 2004;7(1):63-70 (Pubitemid 38332576)
9. Lipkin MJ, Stevens AP, Livingstone DJ, Harris CJ. How large does a compound screening collection need to be? Comb Chem High Throughput Screen 2008;11(6):482-493
10. Kirchmair J, Distinto S, Schuster D, et al. Enhancing drug discovery through in silico screening: strategies to increase true positives retrieval rates. Curr Med Chem 2008;15(20):2040-2053
11. Cavasotto CN, Orry AJ. Ligand docking and structure-based virtual screening in drug discovery. Curr Top Med Chem 2007;7(10):1006-1014 (Pubitemid 47040528)
12. Kontoyianni M, Madhav P, Suchanek E, Seibel W. Theoretical and practical considerations in virtual screening: a beaten field? Curr Med Chem 2008;15(2):107-116
13. Bajorath J. Integration of virtual and high-throughput screening. Nat Rev Drug Discov 2002;1(11):882-894
14. Davies JW, Glick M, Jenkins JL. Streamlining lead discovery by aligning in silico and high-throughput screening. Curr Opin Chem Biol 2006;10(4):343-351 (Pubitemid 44080652)
15. Guido RV, Oliva G, Andricopulo AD. Virtual screening and its integration with modern drug design technologies. Curr Med Chem 2008;15(1):37-46 (Pubitemid 351234626)
16. Mestres J. Virtual screening: a real screening complement to high-throughput screening. Biochem Soc Trans 2002;30(4):797-799 (Pubitemid 35001661)
17. Klebe G. Virtual ligand screening: strategies, perspectives and limitations. Drug Discov Today 2006;11(13-14):580-594 (Pubitemid 43912502)
18. Fox S, Farr-Jones S, Sopchak L, et al. In: Moraga CA, editor, High throughput screening: new users, more cell-based assays, and a host of new tools. High tech business decisions; 2005
19. Fox S, Farr-Jones S, Sopchak L, et al. High-throughput screening: update on practices and success. J Biomol Screen 2006;11(7):864-869 (Pubitemid 44562328)
20. International Human Genome Sequencing Consortium. Finishing the euchromatic sequence of the human genome. Nature 2004;431(7011):931-945 • Intrinsic difficulties of current VS scoring functions to accurately predict binding energy.
21. Overington JP, Al-Lazikani B, Hopkins AL. How many drug targets are there? Nat Rev Drug Discov 2006;5(12):993-996
22. Lundqvist T. The devil is still in the details-driving early drug discovery forward with biophysical experimental methods. Curr Opin Drug Discov Devel 2005;8(4):513-519 (Pubitemid 40917413)
23. Malo N, Hanley JA, Cerquozzi S, et al. Statistical practice in high-throughput screening data analysis. Nat Biotechnol 2006;24(2):167-175 (Pubitemid 43221699)
24. Nell PG, Mundt SM. High throughput-screening in drug discovery. Weinheim: WILEY-VCH Verlag GmBH & Co. KGaA, 2006
25. Hajduk PJ, Huth JR, Tse C. Predicting protein druggability. Drug Discov Today 2005;10(23-24):1675-1682
26. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 2001;46(1-3):3-26 (Pubitemid 33653411)
27. Evers A, Klabunde T. Structure-based drug discovery using GPCR homology modeling: successful virtual screening for antagonists of the alpha1A adrenergic receptor. J Med Chem 2005;48(4):1088-1097 (Pubitemid 40270452)
28. Evers A, Klebe G. Successful virtual screening for a submicromolar antagonist of the neurokinin-1 receptor based on a ligand-supported homology model. J Med Chem 2004;47(22):5381-5392 (Pubitemid 39382788)
29. Cavasotto CN, Orry AJ, Murgolo NJ, et al. Discovery of novel chemotypes to a G-protein-coupled receptor through ligand-steered homology modeling and structure-based virtual screening. J Med Chem 2008;51(3):581-588
30. Richardson CM, Nunns CL, Williamson DS, et al. Discovery of a potent CDK2 inhibitor with a novel binding mode, using virtual screening and initial, structure-guided lead scoping. Bioorg Med Chem Lett 2007;17(14):3880-3885
31. Sandberg EM, Ma X, He K, et al. Identification of 1,2,3,4,5,6- hexabromocyclohexane as a small molecule inhibitor of jak2 tyrosine kinase autophosphorylation [correction of autophophorylation]. J Med Chem 2005;48(7):2526-2533
32. Forino M, Jung D, Easton JB, et al. Virtual docking approaches to protein kinase B inhibition. J Med Chem 2005;48(7):2278-2281 (Pubitemid 40516422)
33. Cavasotto CN, Ortiz MA, Abagyan RA, Piedrafita FJ. In silico identification of novel EGFR inhibitors with antiproliferative activity against cancer cells. Bioorg Med Chem Lett 2006;16(7):1969-1974
34. Shao D, Berrodin TJ, Manas E, et al. Identification of novel estrogen receptor alpha antagonists. J Steroid Biochem Mol Biol 2004;88(4-5):351-360 (Pubitemid 38625599)
35. Thurmond RL, Sun S, Sehon CA, et al. Identification of a potent and selective noncovalent cathepsin S inhibitor. J Pharmacol Exp Ther 2004;308(1):268-276 (Pubitemid 38090727)
36. Murray CW, Callaghan O, Chessari G, et al. Application of fragment screening by X-ray crystallography to beta-secretase. J Med Chem 2007;50(6):1116-1123 (Pubitemid 46496312)
37. Fattorusso R, Jung D, Crowell KJ, et al. Discovery of a novel class of reversible non-peptide caspase inhibitors via a structure-based approach. J Med Chem 2005;48(5):1649-1656 (Pubitemid 40364571)
38. Stoermer MJ. Current status of virtual screening as analysed by target class. Med Chem 2006;2(1):89-112
39. Inglese J, Shamu CE, Guy RK. Reporting data from high-throughput screening of small-molecule libraries. Nat Chem Biol 2007;3(8):438-441 (Pubitemid 47080377)
40. Inglese J, Johnson RL, Simeonov A, et al. High-throughput screening assays for the identification of chemical probes. Nat Chem Biol 2007;3(8):466-479 (Pubitemid 47080373)
41. Shelat AA, Guy RK. Scaffold composition and biological relevance of screening libraries. Nat Chem Biol 2007;3(8):442-446
42. Zhang JH, Chung TD, Oldenburg KR. A simple statistical parameter for use in evaluation and validation of high throughput screening assays. J Biomol Screen 1999;4(2):67-73 • Introduces the key Z-factor as a tool to evaluate HTS experiments.
43. Gagarin A, Makarenkov V, Zentilli P. Using clustering techniques to improve hit selection in high-throughput screening. J Biomol Screen 2006;11(8):903-914 (Pubitemid 44847144)
44. Brideau C, Gunter B, Pikounis B, Liaw A. Improved statistical methods for hit selection in high-throughput screening. J Biomol Screen 2003;8(6):634-647 (Pubitemid 40503572)
45. Yan SF, King FJ, He Y, et al. Learning from the data: mining of large high-throughput screening databases. J Chem Inf Model 2006;46(6):2381-2395 (Pubitemid 46008112)
46. Harper G, Pickett SD. Methods for mining HTS data. Drug Discov Today 2006;11(15-16):694-699
47. Costanzi S, Tikhonova IG, Harden TK, Jacobson KA. Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands. J Comput Aided Mol Des 2008 http://dx.doi.org/10.1007/s10822-008-9218-3
48. Omagari K, Mitomo D, Kubota S, et al. A method to enhance the hit ration by a combination of structure-based drug screening and ligand based drug screening. Adv Appl Bioinform Chem 2008;I:19-28
49. Scapin G. Structural biology and drug discovery. Curr Pharm Des 2006;12(17):2087-2097
50. Rester U. From virtuality to reality - Virtual screening in lead discovery and lead optimization: a medicinal chemistry perspective. Curr Opin Drug Discov Dev 2008;11(4):559-568 (Pubitemid 351950850)
51. Fischer PM. Computational chemistry approaches to drug discovery in signal transduction. Biotechnol J 2008;3(4):452-470 (Pubitemid 351618732)
52. Jorgensen WL. The many roles of computation in drug discovery. Science 2004;303(5665):1813-1818
53. Koppen H. Virtual screening - what does it give us? Curr Opin Drug Discov Devel 2009;12(3):397-407
54. Leach AR, Shoichet BK, Peishoff CE. Prediction of protein-ligand interactions. Docking and scoring: successes and gaps. J Med Chem 2006;49(20):5851-5855
55. McInnes C. Virtual screening strategies in drug discovery. Curr Opin Chem Biol 2007;11(5):494-502 (Pubitemid 47589008)
56. Zoete V, Grosdidier A, Michielin O. Docking, virtual high throughput screening and in silico fragment-based drug design. J Cell Mol Med 2009;13(2):238-248
57. Kitchen DB, Decornez H, Furr JR, Bajorath J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 2004;3(11):935-949 • Comprehensive and authoritative review on docking-based virtual screening. (Pubitemid 39529931)
58. Villoutreix BO, Renault N, Lagorce D, et al. Free resources to assist structure-based virtual ligand screening experiments. Curr Protein Pept Sci 2007;8(4):381-411 (Pubitemid 47321574)
59. Cavasotto CN, Phatak SS. Homology modelling in drug discovery: current trends and applications. Drug Discov Today 2009;14(13-14):676-683
60. Kairys V, Fernandes MX, Gilson MK. Screening drug-like compounds by docking to homology models: a systematic study. J Chem Inf Model 2006;46(1):365-379 (Pubitemid 43282128)
61. Rester U. Dock around the clock-current status of small molecule docking and scoring. QSAR Comb Sci 2006;25(7):605-615 (Pubitemid 44121050)
62. Bacilieri M, Moro S. Ligand-based drug design methodologies in drug discovery process: an overview. Curr Drug Discov Technol 2006;3(3):155-165 (Pubitemid 46425953)
63. Guha R. On the interpretation and interpretability of quantitative structure-activity relationship models. J Comput Aided Mol Des 2008;22(12):857-871
64. Polanski J. Receptor dependent multidimensional QSAR for modeling drug - receptor interactions. Curr Med Chem 2009 [Epub ahead of reprint]
65. Schnecke V, Bostrom J. Computational chemistry-driven decision making in lead generation. Drug Discov Today 2006;11(1-2):43-50 (Pubitemid 43227851)
66. Chen J, Lai L. Pocket v.2: further developments on receptor-based pharmacophore modeling. J Chem Inf Model 2006;46(6):2684-2691 (Pubitemid 46008137)
67. Sun H. Pharmacophore-based virtual screening. Curr Med Chem 2008;15(10):1018-1024
68. Muthas D, Sabnis YA, Lundborg M, Karlen A. Is it possible to increase hit rates in structure-based virtual screening by pharmacophore filtering? An investigation of the advantages and pitfalls of post-filtering. J Mol Graph Model 2008;26(8):1237-1251
69. Barillari C, Marcou G, Rognan D. Hot-spots-guided receptor-based pharmacophores (HS-Pharm): a knowledge-based approach to identify ligand-anchoring atoms in protein cavities and prioritize structure-based pharmacophores. J Chem Inf Model 2008;48(7):1396-1410
70. Joseph-McCarthy D, Baber JC, Feyfant E, et al. Lead optimization via high-throughput molecular docking. Curr Opin Drug Discov Devel 2007;10(3):264-274 (Pubitemid 46800220)
71. Diaz P, Phatak SS, Xu J, et al. 6-Methoxy-N-alkyl isatin acylhydrazone derivatives as a novel series of potent selective cannabinoid receptor 2 inverse agonists: design, synthesis, and binding mode prediction. J Med Chem 2009;52(2):433-444
72. Diaz P, Phatak SS, Xu J, et al. 2,3-dihydro-1-benzofuran derivatives as a novel series of potent selective cannabinoid receptor 2 agonists: design, synthesis, and binding mode prediction through ligand-steered modeling. ChemMedChem 2009. In press http://dx.doi.org/10.1002/cmdc.200900226
73. Rarey M, Kramer B, Lengauer T, Klebe G. A fast flexible docking method using an incremental construction algorithm. J Mol Biol 1996;261(3):470-489 (Pubitemid 26335901)
74. Morris GM, Goodsell DS, Halliday RS, et al. Automated docking using a lamarckian genetic algorithm and empirical binding free energy function. J Comput Chem 1998;19:1639-1662
75. Butler KT, Luque FJ, Barril X. Toward accurate relative energy predictions of the bioactive conformation of drugs. J Comput Chem 2009;30(4):601-610 • It is demonstrated that, contrary to previous observations, most of the ligands bind to receptors with low-energy conformations.
76. Barril X, Fradera X. Incorporating protein flexibility into docking and structure-based drug design. Expert Opin Drug Discov 2006;1:335-349
77. Cavasotto CN, Singh N. Docking and high throughput docking: successes and the challenge of protein flexibility. Curr Comput Aided Drug Design 2008;4(3):221-234
78. Corbeil CR, Moitessier N. Docking ligands into flexible and solvated macromolecules. 3. Impact of input ligand conformation, protein flexibility, and water molecules on the accuracy of docking programs. J Chem Inf Model 2009;49(4):997-1009
79. Jain AN. Effects of protein conformation in docking: improved pose prediction through protein pocket adaptation. J Comput Aided Mol Des 2009;23(6):355-374
80. B-Rao C, Subramanian J, Sharma SD. Managing protein flexibility in docking and its applications. Drug Discov Today 2009;14(7-8):394-400
81. Shoichet BK. Virtual screening of chemical libraries. Nature 2004;432(7019):862-865
82. Tirado-Rives J, Jorgensen WL. Contribution of conformer focusing to the uncertainty in predicting free energies for protein-ligand binding. J Med Chem 2006;49(20):5880-5884 • Intrinsic difficulties of current VS scoring functions to accurately predict binding energy. (Pubitemid 44484934)
83. Mobley DL, Dill KA, Chodera JD. Treating entropy and conformational changes in implicit solvent simulations of small molecules. J Phys Chem B 2008;112(3):938-946
84. Warren GL, Andrews CW, Capelli AM, et al. A critical assessment of docking programs and scoring functions. J Med Chem 2006;49(20):5912-5931 • Exhaustive study of 10 different docking programs with 37 scoring functions.
85. Duca JS, Madison VS, Voigt JH. Cross-docking of inhibitors into CDK2 structures. 1. J Chem Inf Model 2008;48(3):659-668
86. Jain AN. Scoring functions for protein-ligand docking. Curr Protein Pept Sci 2006;7(5):407-420
87. Cavasotto CN, Abagyan RA. Protein flexibility in ligand docking and virtual screening to protein kinases. J Mol Biol 2004;337(1):209-225 (Pubitemid 38270258)
88. Ghosh S, Nie A, An J, Huang Z. Structure-based virtual screening of chemical libraries for drug discovery. Curr Opin Chem Biol 2006;10(3):194-202
89. Yasgar A, Shinn P, Jadhav A, et al. Compound management for quantitative high-throughput screening. JALA Charlottesv Va 2008;13(2):79-89 (Pubitemid 351325745)
90. Briggs MW. Available from: http://www.highresbio.com/pdfs/Briggs-ALA2008. pdf [Cited]
91. Schnur DM. Recent trends in library design: 'rational design' revisited. Curr Opin Drug Discov Devel 2008;11(3):375-380
92. Edwards BS, Bologa C, Young SM, et al. Integration of virtual screening with high-throughput flow cytometry to identify novel small molecule formylpeptide receptor antagonists. Mol Pharmacol 2005;68(5):1301-1310 (Pubitemid 41539986)
93. Xu X, Kasembeli MM, Jiang X, et al. Chemical probes that competitively and selectively inhibit Stat3 activation. PLoS One 2009;4(3):e4783
94. Jennings A, Tennant M. Discovery strategies in a pharmaceutical setting: the application of computational techniques. Expert Opinion Drug Discov 2006;1(7):709-721
95. Olah MM, Bologa CG, Oprea TI. Strategies for compound selection. Curr Drug Discov Technol 2004;1(3):211-220
96. Sun H. A universal molecular descriptor system for prediction of logP, logS, logBB, and absorption. J Chem Inf Comput Sci 2004;44(2):748-757
97. Orry AJ, Abagyan RA, Cavasotto CN. Structure-based development of target-specific compound libraries. Drug Discov Today 2006;11(5-6):261-266
98. Lumley JA. Compound selection and filtering in library design. QSAR Comb Sci 2005;24(9):1066-1075
99. Bleicher KH, Bohm HJ, Muller K, Alanine AI. Hit and lead generation: beyond high-throughput screening. Nat Rev Drug Discov 2003;2(5):369-378 (Pubitemid 37361707)
100. Schneider G, Fechner U. Computer-based de novo design of drug-like molecules. Nat Rev Drug Discov 2005;4(8):649-663 (Pubitemid 41149759)
101. Chen Y, Shoichet BK. Molecular docking and ligand specificity in fragment-based inhibitor discovery. Nat Chem Biol 2009;5(5):358-364
102. Hajduk PJ, Greer J. A decade of fragment-based drug design: strategic advances and lessons learned. Nat Rev Drug Discov 2007;6(3):211-219 (Pubitemid 46344625)
103. Villar HO, Hansen MR. Computational techniques in fragment based drug discovery. Curr Top Med Chem 2007;7(15):1509-1513 (Pubitemid 47618063)
104. Claus BL, Johnson SR. Grid computing in large pharmaceutical molecular modeling. Drug Discov Today 2008;13(13-14):578-583
105. Waller CL, Shah A, Nolte M. Strategies to support drug discovery through integration of systems and data. Drug Discov Today 2007;12(15-16):634-639 (Pubitemid 47243705)
106. Available from: http://accelrys.com/products/scitegic/
107. Available from: www.chemapps.com
108. Available from: http://www.knime.org
109. Available from: www.inforsense.com
110. Available from: http://taverna.sourceforge.net
111. Waszkowycz B. Towards improving compound selection in structure-based virtual screening. Drug Discov Today 2008;13(5-6):219-226
112. Paiva AM, Vanderwall DE, Blanchard JS, et al. Inhibitors of dihydrodipicolinate reductase, a key enzyme of the diaminopimelate pathway of Mycobacterium tuberculosis. Biochim Biophys Acta 2001;1545(1-2):67-77
113. Doman TN, McGovern SL, Witherbee BJ, et al. Molecular docking and high-throughput screening for novel inhibitors of protein tyrosine phosphatase-1B. J Med Chem 2002;45(11):2213-2221
114. Betzi S, Restouin A, Opi S, et al. Protein protein interaction inhibition (2P2I) combining high throughput and virtual screening: application to the HIV-1 Nef protein. Proc Natl Acad Sci USA 2007;104(49):19256-19261
115. Polgar T, Baki A, Szendrei GI, Keseru GM. Comparative virtual and experimental high-throughput screening for glycogen synthase kinase-3beta inhibitors. J Med Chem 2005;48(25):7946-7959 (Pubitemid 41798426)
116. Babaoglu K, Simeonov A, Irwin JJ, et al. Comprehensive mechanistic analysis of hits from high-throughput and docking screens against beta-lactamase. J Med Chem 2008;51(8):2502-2511 (Pubitemid 351628511)
117. Collins FS, Gray GM, Bucher JR. Toxicology. Transforming environmental health protection. Science 2008;319(5865):906-907
118. Glaser V. High throughput screening. 2009; Available from: http://www.bio-itworld.com/uploadedFiles/Bio-IT-World/Bio-IT-Issues/2009/ Jan-Feb/BIW-20090101B-Jan-2009%5B1%5D.pdf [Cited]