Current trends in virtual high throughput screening using ligand-based and structure-based methods

Combinatorial Chemistry and High Throughput Screening

Volumn 14, Issue 10, 2011, Pages 872-888

  Sukumar, Nagamani ^a   Das, Sourav ^b,c  

^a Research Rensselaer Polytechnic Institute   (United States)

^b ST JUDE CHILDREN S RESEARCH HOSPITAL   (United States)

^c PROCTER AND GAMBLE COMPANY   (United States)

Author keywords

Activity cliffs; Applicability domain; Best practices; Chemogenomics; Consensus docking; Descriptors; Model validation; Polypharmacology; QSAR; Site similarity

Indexed keywords

LIGAND;

ANALYTIC METHOD; ARTICLE; BIOINFORMATICS; COST; CRYSTALLOGRAPHY; DRUG MARKETING; HIGH THROUGHPUT SCREENING; MOLECULAR INTERACTION; PRIORITY JOURNAL; QUANTITATIVE STRUCTURE ACTIVITY RELATION; SPECTROSCOPY; VIRTUAL REALITY;

ANIMALS; DRUG DESIGN; HIGH-THROUGHPUT SCREENING ASSAYS; HUMANS; LIGANDS; MODELS, BIOLOGICAL; MODELS, MOLECULAR; PHARMACEUTICAL PREPARATIONS; PHARMACOLOGY; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 80054917823     PISSN: 13862073     EISSN: 18755402     Source Type: Journal    
DOI: 10.2174/138620711797537120     Document Type: Article

References (145)

1. Shao, L.; Wu, L.; Fan, X.; Cheng, Y. Consensus ranking approach to understanding the underlying mechanism with QSAR. J. Chem. Inf. Model., 2010, 50, 1941-1948.
2. Tropsha, A. Best practices for QSAR model development, validation, and exploitation. Molecular Informatics, 2010, 29 (6-7), 476-488.
3. Breneman, C. M. Predictive Cheminformatics: Best Practices for Determining Model Domain Applicability, Sanibel Conference, Sanibel Island, Florida, 2007 http://reccr.chem.rpi.edu/Presentations/Sanibel2007-BestPractices. pdf
4. Maggiora, G.M. On outliers and activity cliffs Why QSAR often disappoints. J. Chem. Inf. Model., 2006, 46, 1535. (Pubitemid 44185680)
5. Guha, R; Van Drie, J. H. Structure-activity landscape index: identifying and quantifying activity cliffs. J. Chem. Inf. Model., 2008, 48, 646-658. (Pubitemid 351535432)
6. Guha, R; Van Drie, J. H. Assessing how well a modeling protocol captures a structure-activity landscape. J. Chem. Inf. Model., 2008, 48, 1716-1728.
7. Hammett, L. P. Some relations between reaction rates and equilibrium constants. Chem. Rev., 1935, 17, 125.
8. Hammett, L. P. The effect of structure upon the reactions of organic compounds. benzene derivatives. J. Amer. Chem. Soc., 1937, 59, 96.
9. Taft, R. W. Polar and steric substituent constants for aliphatic and o-benzoate groups from rates of esterification and hydrolysis of esters. J. Amer. Chem. Soc., 1952, 74, 3120.
10. Hansch, C.; Muir, R. M.; Fujita, T.; Maloney, P. P.; Geiger, F.; Streich, M. The correlation of biological activity of plant growth regulators and chloromycetin derivatives with Hammett constants and partition coefficients. J. Amer. Chem. Soc., 1963, 85, 2817-2824.
11. Hansch, C.; Leo, A., Exploring QSAR. ed.; American Chemical Society: 1995.
12. Kier, L. B.; Hall, L. H., Molecular Connectivity in Chemistry and Drug Research. ed.; Acadmic Press: New York, 1976.
13. Kier, L. B.; Hall, L. H., Molecular Connectivity in Structure- Activity Analysis. ed.; Research Studies Press: Letchworth, 1986.
14. Kier, L. B.; Murray, W. J.; Randi, M.; Hall, L. H. Molecular connectivity I: Relationship to nonspecific local anesthesia. J. Pharm. Sci., 1975, 64, 1971-1974.
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 Delivery Rev., 2001, 46 (1-3), 3.
16. Randi, M. The connectivity index 25 years after. J. Mol. Graph. Model., 2001, 20 (1), 19-35. (Pubitemid 32938276)
17. Elsevier MDL®, San Ramon, CA.
18. Daylight® Chemical Information Systems, Inc. Aliso Viejo, CA.
19. Sybyl® Tripos L.P. St. Louis MO
20. Morgan, H. L. The generation of a unique machine description for chemical structures - A technique developed at Chemical Abstracts Service. J. Chem. Doc., 1965, 5, 107-113.
21. Vogt, M.; Stumpfe, D.; Geppert, H.; Bajorath, J. Scaffold hopping using two-dimensional fingerprints: true potential, black magic, or a hopeless endeavor? Guidelines for virtual screening. J. Med. Chem., 2010, 53 (15), 5707-5715.
22. Barker, E. J.; Buttar, D.; Cosgrove, D. A.; Gardiner, E. J.; Kitts, P.; Willett, P.; Gillet, V. J. Scaffold hopping using clique detection applied to reduced graphs, J. Chem. Inf. Model., 2006, 46 (2), 503-511.
23. Ehrlich. Dtsch. Chem. Ges. 1909, 42, 17.
24. Tirado-Rives, J.; Jorgensen, W. L. Contribution of conformer focusing to the uncertainty in predicting free energies for protein ligand binding. J. Med. Chem., 2006, 49, 5880-5884. (Pubitemid 44484934)
25. Bender, A.; Mussa, H. Y.; Glen, R. C.; Reiling, S. Molecular similarity searching using atom environments, information-based feature selection, and a naïve bayesian classifier. J. Chem. Inf. Comput. Sci., 2004, 44, (1), 170-178.
26. Cramer, I., Richard, D.; Patterson, D. E.; Bunce, J. D. Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteIns. J. Amer. Chem. Soc., 1988, 110, 5959.
27. Cramer, R. D.; Jilek, R. J.; Andrews, K. M. dbtop: Topomer similarity searching of conventional structure databases. J. Molec. Graph. Model., 2002, 20, 447-462. (Pubitemid 34312060)
28. Jilek, R. J.; Cramer, R. D. Topomers: A validated protocol for their self-consistent generation. J. Chem. Inf. Comput. Sci., 2004, 44, 1221-1227.
29. Moffat, K.; Gillet, V. J.; Whittle, M.; Bravi, G.; Leach, A. R. A comparison of field-based similarity searching methods: CatShape, FBSS, and ROCS, J. Chem. Inf. Model., 2008, 48 (4), 719-729. (Pubitemid 351757739)
30. McGaughey, G. B.; Sheridan, R. P.; Bayly, C. I.; Culberson, J.; Kreatsoulas, C.; Lindsley, S.; Maiorov, V.; Truchon, J.-F.; Cornell, W. D. Comparison of topological, shape, and docking methods in virtual screening, J. Chem. Inf. Model., 2007 47 (4), 1504-1519. (Pubitemid 47210053)
31. Saunders, R. A.; Platts, J. A. Correlation and prediction of critical micelle concentration using polar surface area and LFER methods. J. Phys. Org. Chem., 2004, 17 (5), 431-438.
32. Palm, K.; Luthman, K.; Ungell, A. L.; Strandlund, G.; Artursson, P. Correlation of drug absorption with molecular surface properties. J. Pharmaceut. Sci., 1996, 85 (1), 32-39. (Pubitemid 26029068)
33. Clark, D. E. Rapid calculation of polar molecular surface area and its application to the prediction of transport phenomena. 1. Prediction of intestinal absorption. J. Pharm. Sci., 1999, 88, 807-814. (Pubitemid 29398550)
34. Clark, D. E. Rapid calculation of polar molecular surface area and its application to the prediction of transport phenomena. 2. Prediction of blood-brain barrier penetration. J. Pharm. Sci., 1999, 88 (8), 815-821. (Pubitemid 29398551)
35. Kelder, J.; Grootenhuis, P. D. J.; Bayada, D. M.; Delbressine, L. P. C.; Ploemen, J.-P. Polar molecular surface as a dominating determinant for oral absorption and brain penetration of drugs. Pharmaceut. Res., 1999, 16 (10), 1514.
36. Palm, K.; Stenberg, P.; Luthman, K.; Artursson, P. Polar molecular surface properties predict the intestinal absorption of drugs in humans. Pharmac. Res., 1997, 14 (5), 568.
37. Camenisch, G.; Folkers, G.; van de Waterbeemd, H. Pharm. Acta Helvet., 1996, 71, 309-327.
38. Veber, D. F.; Johnson, S. R.; Cheng, H. Y.; Smith, B. R.; Ward, K. W.; Kopple, K. D. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem., 2002, 45 (12), 2615-2623. (Pubitemid 34595232)
39. Eisenberg, D.; McClachlan, A. Solvation energy in protein folding and binding. Nature, 1986, 319, 199-203. (Pubitemid 16128783)
40. Ehresmann, B.; Groot, M. J. d.; Alex, A.; Clark, T. New molecular descriptors based on local properties at the molecular surface and a boiling-point model derived from them. J. Chem. Inf. Comput. Sci., 2004, 44 (2), 658-668.
41. Clark, T. QSAR and QSPR based solely on surface properties? J. Molec. Graph. Model., 2004, 22, 519-525. (Pubitemid 38746577)
42. Whitehead, C. E.; Breneman, C. M.; Sukumar, N.; Ryan, M. D. Transferable atom equivalent multicentered multipole expansion method. J. Comput. Chem., 2003, 24(4), 512-529.
43. Sukumar, N.; Breneman, C. M. QTAIM in drug discovery and protein modeling, in The Quantum Theory of Atoms in Molecules: From Solid State to DNA and Drug Design, Matta, C.F.; Boyd, R.J., Eds.; Wiley-VCH, 2007, pp.471-498.
44. Bader, R. F. W. Atoms in Molecules: A Quantum Theory; Oxford Press: Oxford, 1990.
45. Matta, C. F.; Boyd, R. J. The Quantum Theory of Atoms in Molecules: From Solid State to DNA and Drug Design; Wiley- VCH: Weinheim, 2007.
46. Politzer, P.; Murray, J. S.; Peralta-Inga, Z. Molecular surface electrostatic potentials in relation to noncovalent interactions in biological systems. Int. J. Quantum Chem., 2001, 85 (6), 676-684. (Pubitemid 33105812)
47. Politzer, P.; Truhlar, D. G. Chemical Applications of Atomic and Molecular Electrostatic Potential; Plenum Press: New York, 1981.
48. Politzer, P.; Murray, J. S. The average local ionization energy: concepts and applications, in Theoretical Aspects of Chemical Reactivity, Toro-Labbé, A., Ed.; Theoret. Computat. Chem., Elsevier, 2007, 19, 119-137.
49. Murray, J. S.; Politzer, P.; Famini, G. R. Theoretical alternatives to linear solvation energy relationships. J. Molec. Struct. (THEOCHEM), 1998, 454 (2-3), 299-306. (Pubitemid 128411957)
50. Fukui, K. Role of frontier orbitals in chemical reactions. Science, 1987, 218, 747-754.
51. Parr, R. G.; Yang, W. Density functional approach to the frontierelectron theory of chemical reactivity. J. Amer. Chem. Soc., 1984, 106, 4049-4050.
52. Bergeron, C.; Hepburn, T.; Sundling, M.; Sukumar, N.; Bennett, K. P.; Breneman, C. Prediction of peptide bonding affinity: kernel methods for nonlinear modeling. Protein Peptide Lett., in press.
53. Rush, T. S.; Grant, J. A.; Mosyak, L.; Nicholls, A. A shape-based 3-d scaffold hopping method and its application to a bacterial protein-protein interaction. J. Med. Chem., 2005, 48, (5), 1489-1495. (Pubitemid 40364556)
54. Cruciani, G.; Watson, K. Comparative molecular field analysis using GRID force-field and GOLPE variable selection methods in a study of inhibitors of glycogen phosphorylase b. J. Med. Chem., 1994, 37, 2589-2601. (Pubitemid 24276786)
55. Crivori, P.; Cruciani, G.; Carrupt, P.-A.; Testa, B. Predicting blood-brain barrier permeation from three-dimensional molecular structure. J. Med. Chem., 2000, 43, 2204-2216. (Pubitemid 30354545)
56. Pastor, M.; Cruciani, G.; McLay, I.; Pickett, S.; Clementi, S. GRid- INdependent Descriptors (GRIND): A novel class of alignmentindependent three-dimensional molecular descriptors. J. Med. Chem., 2000, 43, 3233-3243.
57. Zauhar, R.; Moyna, G.; Tian, L.; Li, Z.; Welsh, W. J. Shape Signatures: A new approach to computer-aided ligand- and receptor-based drug design. J. Med. Chem., 2003, 46, 5674-5690. (Pubitemid 37543318)
58. Nagarajan, K.; Zauhar, R.; Welsh, W. J. Enrichment of ligands for the serotonin receptor using the shape signatures approach. J. Chem. Inf. Model., 2005, 45, 49-57. (Pubitemid 40736955)
59. Breneman, C. M.; Sundling, C. M.; Sukumar, N.; Shen, L.; Katt, W. P.; Embrechts, M. J. New developments in PEST shape/property hybrid descriptors. J. Comput.-Aided Mol. Des., 2003, 17, 231-240. (Pubitemid 37062784)
60. Sundling, C. M.; Sukumar, N.; Zhang, H.; Embrechts, M. J.; Breneman, C. M. Wavelets in chemistry and cheminformatics. Rev. Comput. Chem., 2006, 22, 295-329. (Pubitemid 44568977)
61. Ballester, P. J.; Richards, W. G. Ultrafast shape recognition for similarity search in molecular databases, Proc. R. Soc. A, 2007, 463, 1307-1321.
62. Ballester, P. J.; Richards, W. G. Ultrafast shape recognition to search compound databases for similar molecular shapes. J. Comput. Chem., 2007, 28, 1711-1723. (Pubitemid 47146547)
63. Ballester, P. J.; Finn, P. W.; Richards W. G. Ultrafast shape recognition: evaluating a new ligand-based virtual screening technology. J. Mol. Graph. Model., 2009, 27, 836-845.
64. Armstrong, M. S.; Morris, G. M.; Finn, P. W.; Sharma, R.; Richards, W. G. Molecular similarity including chirality. J. Mol. Graph. Model., 2009, 28, 368-370.
65. Armstrong, M. S.; Morris, G. M.; Finn, P. W.; Sharma, R.; Moretti, L.; Cooper, R. I.; Richards, W. G. ElectroShape: fast molecular similarity calculations incorporating shape, chirality and electrostatics. J. Comput.-Aided Mol. Des., 2010, 24, 789-801.
66. Cannon, E. O.; Nigsch, F.; Mitchell, J. B. O. A novel hybrid ultrafast shape descriptor method for use in virtual screening. Chem. Central J., 2008, 2, 3.
67. Hert, J.; Willett, P.; Wilton, D. J.; Acklin, P.; Azzaoui, K.; Jacoby, E.; Schuffenhauer, A. Comparison of fingerprint-based methods for virtual screening using multiple bioactive reference structures. J. Chem. Inf. Comput. Sci., 2004, 44, 1177-1185.
68. Arif, S. M.; Holliday, J. D.; Willett, P. Analysis and use of fragment-occurrence data in similarity-based virtual screening. J. Comput.-Aided Mol. Des., 2009, 23, 655-668.
69. Ginn, C. M. R.; Willett, P.; Bradshaw, J. Combination of molecular similarity measures using data fusion. Persp. Drug Disc. Design., 2000, 20.
70. Salim, N.; Holliday, J.; Willett, P. Combination of fingerprintbased similarity coefficients using data fusion. J. Chem. Inf. Comput. Sci., 2003, 43, 435-442.
71. Godden, J. W.; Furr, J. R.; Xue, L.; Stahura, F. L.; Bajorath, J. Molecular similarity analysis and virtual screening by mapping of consensus positions in binary-transformed chemical descriptor spaces with variable dimensionality. J. Chem. Inf. Comput. Sci., 2004.
72. Baurin, N.; Mozziconacci, J. C.; Arnoult, E.; Chavatte, P.; Marot, C., Morin-Allory, L. 2D QSAR consensus prediction for high throughput virtual screening. An application to COX-2 inhibition modeling and screening of the NCI database. J. Chem. Inf. Comput. Sci., 2004, 44, 276-285.
73. Raymond, J. W.; Jalaie, M.; Bradley, M. P. Conditional probability: a new fusion method for merging disparate virtual screening results. J. Chem. Inf. Comput. Sci., 2004, 44, 601-609.
74. Huang, C.; Embrechts, M. J.; Sukumar, N.; Breneman, C. M. Data fusion and auto-fusion for quantitative structure-activity relationship (QSAR). Lecture Notes Comput. Sci. ICANN (Springer), 2007, 1, 628-637.
75. Baber, J. C.; Shirley, W. A.; Gao, Y.; Feher, M. The use of consensus scoring in ligand-based virtual screening. J. Chem. Inf. Model., 2006, 46, 277-288. (Pubitemid 43285403)
76. Embrechts, M. J.; Arciniegas, F. A.; Ozdemir, M.; Kewley, R. H. "Data mining for molecules with 2-D neural network sensitivity analysis". Int. J. Smart Eng. Syst. Des., 2003, 5, 225-239.
77. Embrechts, M. J.; Bress, R. C.; Kewley, R. H. Feature selection via senitivity analysis with direct kernel PLS" Feature Selection in Machine Learning. Guyon, I.; Gunn, S., Eds., Springer Verlag, Stud. Fuzz. 2006, 207, 447-462.
78. Vapnik, V, N. Statistical Learning Theory, Wiley-Interscience, 1966.
79. Norinder, U. Single and domain made variable selection in 3D QSAR applications. J. Chemom., 1996, 10, 95-105. (Pubitemid 126469176)
80. Kubinyi, H.; Hamprecht, F.; Mietzner, T. Three-dimensional quantitative similarity-activity relationships (3D QSiAR) from SEAL similarity matrices. J. Med. Chem., 1998, 41, 2553-2564. (Pubitemid 28321904)
81. Kubinyi, H. Why Models Fail http://www.kubinyi.de/sanfrancisco-09-06.pdf
82. Golbraikh, A.; Tropsha, A. "Beware of q2 !". J. Mol. Graph. Model., 2002, 20, 269-276.
83. Tetko, I. V.; Sushko, I.; Pandey, A. K.; Zhu, H.; Tropsha, A.; Papa, E.; Öberg, T.; Todeschini, R.; Fourches, D.; Varnek, A. Critical assessment of QSAR models of environmental toxicity against Tetrahymena pyriformis: focusing on applicability domain and overfitting by variable selection. J. Chem. Inf. Model., 2008, 48 (9), 1733-1746.
84. Nikolova, N.; Jaworska, J. Approaches to measure chemical similarity-a Review. QSAR Comb. Sci., 2003, 22, 1006-1026.
85. Tropsha, A.; Gramatica, P.; Gombar, V. The importance of being earnest. QSAR Comb. Sci., 2003, 22, 69-77.
86. Johnson, S. R. The trouble with QSAR (or how i learned to stop worrying and embrace fallacy). J. Chem. Inf. Model., 2008, 48 (1), 25-26. (Pubitemid 351271047)
87. Johnson, M.; Maggiora, G. Concepts and Applications of Molecular Similarity. John Wiley, New York, 1990.
88. Martin, Y. C.; Kofron, J.L.; Traphagen, L. M. Do structurally similar molecules have similar biological activity? J. Med. Chem., 2002, 45, 4350-4358. (Pubitemid 35025581)
89. Peltason, L.; Bajorath, J. SAR Index: quantifying the nature of structure-activity relationships. J. Med. Chem., 2007, 50, 5571-5578. (Pubitemid 350106013)
90. Klabunde, T. Chemogenomic approaches to drug discovery: similar receptors bind similar ligands. Br. J. Pharmacol., 2007, 152 (1), 5-7. (Pubitemid 47339899)
91. Rognan, D. Chemogenomic approaches to rational drug design. Br. J. Pharmacol., 2007, 152, 38-52. (Pubitemid 47339898)
92. Fliri, A. F.; Loging, W. T.; Thadeio, P. F.; Volkmann, R. A. Biospectra analysis: Model proteome characterization for linking molecular structure and biological response. J. Med. Chem. 2005, 48, 6918-6925. (Pubitemid 41533113)
93. Fliri, A. F.; Loging, W. T.; Thadeio, P. F.; Volkmann, R. A. Biological spectra analysis: Linking biological activity profiles to molecular structure. Proc. Nat. Acad. Sci. USA, 2005, 102, 261-266. (Pubitemid 40116755)
94. Wawer, M.; Peltason, L.; Weskamp, N.; Teckentrup, A.; Bajorath, J. Structure-activity relationship anatomy by network-like similarity graphs and local structure-activity relationship indices, J. Med. Chem., 2008, 51, 6075-6084.
95. Martin, Y.C. Let's not forget tautomers. J. Comput.-Aided Mol. Des., 2009, 23, 693-704.
96. Kalliokoski, T.; Salo, H. S.; Lahtela-Kakkonen, M.; Poso, A. The effect of ligand-based tautomer and protomer prediction on structure-based virtual screening. J. Chem. Inf. Model., 2009, 49, 2742-2748.
97. ten Brink, T.; Exner, T. E. Influence of protonation, tautomeric, and stereoisomeric states on protein-ligand docking results. J. Chem. Inf. Model., 2009, 49, 1535-1546.
98. Clark, R. D.; Shepphird, J. K.; Holliday, J. The effect of structural redundancy in validation sets on virtual screening performance. J. Chemom., 2009, 23, 471-478.
99. Milletti, F.; Vulpetti A. Tautomer preference in PDB complexes and its impact on structure-based drug discovery. J. Chem. Inf. Model., 2010, 50, 1062-1074
100. Oellien, F.; Cramer, J.; Beyer, C.; Ihlenfeldt, W. D.; Selzer, P. M. The impact of tautomer forms on pharmacophore-based virtual screening. J. Chem. Inf. Model., 2006, 46, 2342-2354. (Pubitemid 46008109)
101. Huang, N.; Shoichet, B. K.; Irwin, J. J. Benchmarking sets for molecular docking. J. Med. Chem., 2006, 49, 6789-6801. (Pubitemid 44749746)
102. Kolb, P.; Ferreira, R. S.; Irwin, J. J.; Shoichet, B. K. Docking and chemoinformatic screens for new ligands and targets. Curr Opin Biotechnol., 2009, 20 (4), 429-436.
103. Babaoglu, K.; Simeonov, A.; Irwin, J.; Nelson, M. E.; Feng, B.; Thomas, C. J.; Cancian, L.; Costi, P.; Maltby, D. A.; Jadhav, A.; Inglese, J.; Austin, C.; Shoichet, B. K. Comprehensive mechanistic analysis of hits from high throughput and docking screens against b-lactamase. J. Med. Chem., 2008, 51, 2502-2511. (Pubitemid 351628511)
104. Hermann, J. C.; Marti-Arbona, R.; Fedorov, A. A.; Fedorov, E.; Almo, S. C.; Shoichet, B. K.; Raushel, F. M. Structure-based activity prediction for an enzyme of unknown function. Nature, 2007, 448, 775-779. (Pubitemid 47266333)
105. Xiang, D. F.; Kolb, P.; Fedorov, A. A.; Meier, M. M.; Fedorov, L. V.; Nguyen, T. T.; Sterner, R.; Almo, S. C.; Shoichet, B. K.; Raushel, F. M. Functional annotation and three-dimensional structure of Dr0930 from Deinococcus radiodurans, a close relative of phosphotriesterase in the amidohydrolase superfamily. Biochemistry, 2009, 48, 2237-2247.
106. Kolb, P.; Rosenbaum, D. M.; Irwin, J. J.; Fung, J.; Kobilka, B. K.; Shoichet, B. K. Structure-based discovery of b2-adrenergic receptor ligands. Proc. Natl. Acad. Sci. USA, 2009, 106, 6843-6848.
107. Sabio, M.; Jones, K.; Topiol, S. Use of the X-ray structure of the b2-adrenergic receptor for drug discovery. Part 2. Identification of active compounds. Bioorg. Med. Chem. Lett., 2008, 18, 5391-5395.
108. Jain, A. N. Effects of protein conformation in docking: improved pose prediction through protein pocket adaptation. J. Comput.-Aided Mol. Des. 2009, 23 (6), 355-374.
109. Chen, Y.; Shoichet B. K. Molecular docking and ligand specificity in fragment-based inhibitor discovery. Nat. Chem. Biol., 2009, 5(5), 358-364.
110. Mpamhanga, C. P.; Spinks, D.; Tulloch, L. B.; Shanks, E. J.; Robinson, D. A.; Collie I. T.; Fairlamb, A. H.; Wyatt, P. G.; Frearson, J. A.; Hunter, W. N.; Gilbert, I. H.; Brenk. R. One scaffold, three binding modes: novel and selective pteridine reductase 1 inhibitors derived from fragment hits discovered by virtual screening. J. Med. Chem., 2009, 52(14), 4454-4465.
111. Ekonomiuk, D.; Su, X. C.; Ozawa, K.; Bodenreider, C.; Lim, S. P.; Yin, Z.; Keller, T. H.; Beer, D.; Patel, V.; Otting, G. Discovery of a non-peptidic inhibitor of West Nile virus NS3 protease by high throughput docking. PLoS Negl. Trop. Dis., 2009, 3, e356.
112. Krüger, D. M.; Evers, A. Comparison of structure-and ligandbased virtual screening protocols considering hit list complementarity and enrichment factors. Chem. Med. Chem., 2010, 5(1), 148-158.
113. Irwin, J. J.; Shoichet, B. K.; Mysinger, M. M.; Huang, N.; Colizzi, F.; Wassam, P.; Cao, Y. Automated docking screens: A feasibility study. J. Med. Chem., 2009, 52 (18), 5712-5720.
114. Hartshorn, M. J.; Verdonk, M. L.; Chessari, G.; Brewerton, S. C.; Mooij, W. T.; Mortenson, P. N.; Murray, C. W. Diverse, highquality test set for the validation of protein-ligand docking performance. J. Med. Chem., 2007, 50, 726-741 (Pubitemid 46332985)
115. Moustakas, D. T.; Lang, P. T.; Pegg, S.; Pettersen, E.; Kuntz, I. D.; Brooijmans, N.; Rizzo, R. C. Development and validation of a modular, extensible docking program: DOCK 5. J. Comput.-Aided Mol. Des., 2006, 20, 601-619. (Pubitemid 46023925)
116. Fan, H.; Irwin, J. J.; Webb, B. M.; Klebe, G.; Shoichet, B. K.; Sali, A. Molecular docking screens using comparative models of proteins. J. Chem. Inf. Model., 2009, 49 (11), 2512-2527.
117. Olsson, T. S. G.; Williams, M. A.; Pitt, W. R.; Ladbury, J. E. The thermodynamics of protein-ligand interaction and solvation: Insights for ligand design. J. Mol. Biol., 2008, 384, 1002-1017.
118. Gilli, P.; Ferretti, V.; Gilli, G.; Borea, P. A. Enthalpy-entropy compensation in drug-receptor binding. J. Phys. Chem., 1994, 98, 1515-1518.
119. Das, S.; Krein, M. P.; Breneman, C. M. Binding affinity prediction with property-encoded shape distribution signatures. J. Chem. Inf. Model., 2010, 50 (2), 298-308.
120. Gohlke, H.; Klebe, G. Approaches to the description and prediction of the binding affinity of small-molecule ligands to macromolecular receptors. Angew. Chem., Int. Ed., 2002, 41, 2644-2676.
121. Wang, R.; Lu, Y.; Wang, S. Comparative evaluation of 11 scoring functions for molecular docking. J. Med. Chem., 2003, 46, 2287-2303. (Pubitemid 36637914)
122. Wang, R.; Lai, L.; Wang, S. Further development and validation of empirical scoring functions for structure-based binding affinity prediction. J. Comput.-Aided Mol. Des., 2002, 16, 11-26. (Pubitemid 34855041)
123. Weill, A.; Rognan, D. Alignment-free ultra-high throughput comparison of druggable protein-ligand binding sites. J. Chem. Inf. Model., 2010, 50, 123-135.
124. Feldman, H. J.; Labute, P. Pocket similarity: Are-carbons enough? J. Chem. Inf. Model., 2010, 50 (8), 1466-1475.
125. De Franchi, E.; Schalon, C; Messa, M.; Onofri, F.; Benfenati, F. Binding of protein kinase inhibitors to synapsin i inferred from pair-wise binding site similarity measurements. PLoS ONE, 2010, 5(8), e12214.
126. Reisen, F.; Weisel, M.; Kriegl, J. M.; Schneider, G. Self-organizing fuzzy graphs for structure-based comparison of protein pockets. J. Proteome Res., 2010, 9, 6498-6510.
127. Keiser, M. J.; Setola, V.; Irwin, J. J.; Laggner, C.; Abbas, A. I.; Hufeisen, S. J.; Jensen, N. H.; Kuijer, M. B.; Matos, R. C.; Tran, T. B.; Whaley, R.; Glennon, R. A.; Hert, J.; Thomas, K. L.; Edwards, D. D.; Shoichet, B. K.; Roth, B. L. Predicting new molecular targets for known drugs. Nature, 2009, 462, 175-181.
128. Paolini, G. V.; Shapland, R. H.; van Hoorn, W. P.; Mason, J. S.; Hopkins, A. L. Global mapping of pharmacological space. Nat. Biotech., 2006, 24, 805-815. (Pubitemid 44086621)
129. Kinnings, S. L.; Liu, N.; Buchmeier, N.; Tonge, P. J.; Xie, L.; Bourne, P. E. Drug discovery using chemical systems biology: repositioning the safe medicine Comtan to treat multi-drug and extensively drug resistant tuberculosis. PLoS Comput. Biol., 2009, 5(7), e1000423.
130. Das, S.; Kokardekar, A.; Breneman, C. M. Rapid comparison of protein binding site surfaces with property encoded shape distributions. J. Chem. Inf. Model., 2009, 49 (12), 2863-2872.
131. Sukumar, N.; Krein, M.; Breneman, C. M. Bioinformatics and cheminformatics: Where do the twain meet? Curr. Opinion Drug Disc. Devel., 2008, 11 (3), 311-319. (Pubitemid 351572440)
132. Bredel, M.; Jacoby, E. Chemogenomics: An emerging strategy for rapid target and drug discovery. Nat. Rev. Genet., 2004, 5 (4), 262-275. (Pubitemid 38435622)
133. Mestres, J. Computational chemogenomics approaches to systematic knowledge-based drug discovery. Curr. Opin. Drug Disc. Dev., 2004, 7 (3), 304-313. (Pubitemid 44120947)
134. Oloff, S.; Zhang, S.; Sukumar, N.; Breneman, C.; Tropsha, A. Chemometric analysis of ligand receptor complementarity: Identifying complementary ligands based on receptor information (CoLiBRI). J. Chem. Inf. Model., 2006, 46 (2), 844-851.
135. Xie, L.; Bourne, P.E. Detecting evolutionary relationships across existing fold space, using sequence order-independent profileprofile alignments. Proc. Natl. Acad. Sci. USA, 2008, 105, 5441-5446. (Pubitemid 351753882)
136. Ren, J.; Xie, L.; Li, W. W.; Bourne, P. E. SMAP-WS: a parallel web service for structural proteome-wide ligand-binding site comparison, Nucleic Acids Res., 2010, 38 (Web Server issue), W441-W444.
137. Das, S.; Krein M. P.; Breneman, C. M. PESDserv: a server for high throughput comparison of protein binding site surfaces. Bioinformatics, 2010, 26 (15), 1913-1914: http://reccr.chem.rpi.edu/Software/pesdserv/
138. Martin, R. E.; Green, L. G.; Guba, W.; Kratochwil, N.; Christ, A. Discovery of the first nonpeptidic, small-molecule, highly selective somatostatin receptor subtype 5 antagonists: a
139. Gloriam D. E.; Foord, S. M.; Blaney, F. E.; Garland, S. L. Definition of the G-protein-coupled receptor transmembrane bundle binding pocket and calculation of receptor similarities for drug design. J. Med. Chem., 2009, 52 (14), 4429-4442.
140. Kinnings, S.L.; Jackson, R.M. Binding Site Similarity Analysis for the Functional Classification of the Protein Kinase Family. J. Chem. Inf. Model., 2009, 49 (2), 318-329.
141. Sheridan, R. P.; Nam, K.; Maiorov, V. N.; McMasters, D. R.; Cornell, W. D. QSAR models for predicting the similarity in binding profiles for pairs of protein kinases and the variation of models between experimental data sets. J. Chem. Inf. Model., 2009, 49, 1974-1985.
142. Hopkins, A. L. Network pharmacology: the next paradigm in drug discovery. Nature Chem. Biol., 2008, 4, 682-690.
143. Milletti, F.; Vulpetti, A. Predicting polypharmacology by binding site similarity: from kinases to the protein universe. J. Chem. Inf. Model., 2010, 50 (8), 1418-1431.
144. Belongie, S.; Malik, J.; Puzicha, J. Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24, 509-522. (Pubitemid 34448769)
145. 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 Disc., 2004, 3, 353-359. (Pubitemid 38499762)