Computational chemistry approaches to drug discovery in signal tansduction

Biotechnology Journal

Volumn 3, Issue 4, 2008, Pages 452-470

  Fischer, Peter M ^a  

^a UNIVERSITY OF NOTTINGHAM   (United Kingdom)

Author keywords

Computational chemistry; Molecular modeling; Molecular recognition; Structure based drug design; Virtual screening

Indexed keywords

CHEMISTRY; COMPUTATIONAL METHODS; LIGANDS; MOLECULAR MODELING; MOLECULAR RECOGNITION;

COMPUTATIONAL CHEMISTRY; DRUG DESIGN; VIRTUAL SCREENING;

DRUG PRODUCTS;

1,3,7 TRIAZASPIRO[4.4]NONANE DERIVATIVE; 1,4 DIAZEPANE 2,5 DIONE; 4 (4 PHENYLSULFANYLPHENYL)PYRIDINE DERIVATIVE; CYCLIN DEPENDENT KINASE 2; CYTOCHROME P450; DOPAMINE 2 RECEPTOR BLOCKING AGENT; DOPAMINE 2 RECEPTOR STIMULATING AGENT; DOPAMINE RECEPTOR STIMULATING AGENT; FLUPHENAZINE; G PROTEIN COUPLED RECEPTOR; INTERCELLULAR ADHESION MOLECULE 1; LFA 878; LIGAND; LYMPHOCYTE FUNCTION ASSOCIATED ANTIGEN 1; MEVINOLIN; PROTEIN INHIBITOR; PROTEIN KINASE; PROTEIN KINASE INHIBITOR; RECEPTOR BLOCKING AGENT; THROMBIN INHIBITOR;

BIOCHEMISTRY; DRUG BINDING SITE; DRUG DESIGN; DRUG POTENCY; DRUG RECEPTOR BINDING; DRUG RESEARCH; DRUG SCREENING; DRUG TARGETING; HUMAN; MACROMOLECULE; MATHEMATICAL COMPUTING; MOLECULAR BIOLOGY; NONHUMAN; NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY; PHYSICAL CHEMISTRY; PREDICTION; PRIORITY JOURNAL; PROTEIN CONFORMATION; QUANTITATIVE STRUCTURE ACTIVITY RELATION; REVIEW; SIGNAL TRANSDUCTION; STRUCTURAL HOMOLOGY; X RAY CRYSTALLOGRAPHY;

RUMEX;

EID: 42949106090     PISSN: 18606768     EISSN: None     Source Type: Journal    
DOI: 10.1002/biot.200700259     Document Type: Review

References (180)

1. 1 Jimeno, A., Hidalgo, M., Multitargeted therapy: Can promiscuity be praised in an era of political correctness? Crit. Rev. Oncol. Hematol. 2006, 59, 150–158.
2. 2 Barril, X., Soliva, R., Molecular modelling. Mol. BioSyst. 2006, 2, 660–681.
3. 3 Engel, T., Basic overview of chemoinformatics. J. Chem. Inf. Model. 2006, 46, 2267–2277.
4. 4 Cheng, Y.‐C., Prusoff, W. H., Relation between the inhibition constant IK1) and the concentration of inhibitor which causes fifty per cent inhibition (I50) of an enzymic reaction. Biochem. Pharmacol. 1973, 22, 3099–3108.
5. 5 Kaplan, A. P., Bartlett, P. A., Synthesis and evaluation of an inhibitor of carboxypeptidase A with a Ki value in the femtomolar range. Biochemistry 1991, 30, 8165–8170.
6. 6 Copeland, R. A., Pompliano, D. L., Meek, T. D., Drug‐target residence time and its implications for lead optimization. Nat. Rev. Drug Discov. 2006, 5, 730–739.
7. 7 Hopkins, A. L., Groom, C. R., Alex, A., Ligand efficiency: A useful metric for lead selection. Drug Discov. Today 2004, 9, 430–431.
8. 8 Kuntz, I. D., Chen, K., Sharp, K. A., Kollman, P. A., The maximal affinity of ligands. Proc. Natl. Acad. Sci. USA 1999, 96, 9997–10002.
9. 9 Palm, K., Luthman, K., Ungell, A. L., Strandlund, G. et al., Correlation of drug absorption with molecular surface properties. J. Pharm. Sci. 1996, 85, 32–39.
10. 10 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 Deliv. Rev. 1997, 23, 3–25.
11. 11 Veber, D. F., Johnson, S. R., Cheng, H.‐Y., Smith, B. R. et al., Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem. 2002, 45, 2615–2623.
12. 12 Vieth, M., Siegel, M. G., Higgs, R. E., Watson, I. A. et al., Characteristic physical properties and structural fragments of marketed oral drugs. J. Med. Chem. 2004, 47, 224–232.
13. 13 Lipinski, C. A., Filtering in drug discovery. Ann. Rep. Comput. Chem. 2005, 1, 155–168.
14. 14 Muegge, I., Selection criteria for drug‐like compounds. Med. Res. Rev. 2003, 23, 302–321.
15. 15 Jimenez‐Sanchez, G., Childs, B., Valle, D., Human disease genes. Nature 2001, 409, 853–855.
16. 16 Hopkins, A. L., Groom, C. R., The druggable genome. Nat. Rev. Drug. Discov. 2002, 1, 727–730.
17. 17 Bohacek, R. S., McMartin, C., Guida, W.C., The art and practice of structure‐based drug design: A molecular modeling perspective. Med. Res. Rev. 1996, 16, 3–50.
18. 18 Paolini, G. V., Shapland, R. H., van Hoorn, W. P., Mason, J. S. et al., Global mapping of pharmacological space. Nat. Biotechnol. 2006, 24, 805–815.
19. 19 Cleves, A. E., Jain, A. N., Robust ligand‐based modeling of the biological targets of known drugs. J. Med. Chem. 2006, 49, 2921–2938.
20. 20 Rognan, D., Development and virtual screening of target libraries. J. Physiol. Paris 2006, 99, 232–244.
21. 21 Allen, F. H., Taylor, R., Research applications of the Cambridge Structural Database (CSD). Chem. Soc. Rev. 2004, 33, 463–475.
22. 22 Davis, A. M., Teague, S. J., Kleywegt, G. J., Application and Limitations of X‐ray crystallographic data in structurebased ligand and drug design. Angew. Chem. Int. Ed. 2003, 42, 2718–2736.
23. 23 Hillisch, A., Pineda, L. F., Hilgenfeld, R., Utility of homology models in the drug discovery process. Drug Discov. Today 2004, 9, 659–669.
24. 24 Koehl, P., Levitt, M., Sequence variations within protein families are linearly related to structural variations. J. Mol. Biol. 2002, 323, 551–562.
25. 25 Dunbrack, R. L. Jr., Sequence comparison and protein structure prediction. Curr. Opin. Struct. Biol. 2006, 16, 374–384.
26. 26 Sali, A., Potterton, L., Yuan, F., van Vlijmen, H. et al., Evaluation of comparative protein modeling by MODELLER. Proteins 1995, 23, 318–326.
27. 27 Schwede, T., Kopp, J., Guex, N., Peitsch, M. C., SWISS‐MODEL: An automated protein homology‐modeling server. Nucleic Acids Res. 2003, 31, 3381–3385.
28. 28 Pieper, U., Eswar, N., Davis, F. P., Braberg, H. et al., MODBASE: A database of annotated comparative protein structure models and associated resources. Nucleic Acids Res. 2006, 34, D 291–295.
29. 29 Oshiro, C., Bradley, E. K., Eksterowicz, J., Evensen, E. et al., Performance of 3D‐database molecular docking studies into homology models. J. Med. Chem. 2004, 47, 764–767.
30. 30 Palczewski, K., Kumasaka, T., Hori, T., Behnke, C. A. et al., Crystal structure of rhodopsin: A G protein‐coupled receptor. Science 2000, 289, 739–745.
31. 31 Cherezov, V., Rosenbaum, D. M., Hanson, M. A., Rasmussen, S. G. et al., High‐resolution crystal structure of an engineered human beta2‐adrenergic G protein‐coupled receptor. Science 2007, 318, 1258–1265.
32. 32 Rasmussen, S. G., Choi, H. J., Rosenbaum, D. M., Kobilka, T. S. et al., Crystal structure of the human beta2 adrenergic G‐protein‐coupled receptor. Nature 2007, 450, 383–387.
33. 33 Reggio, P. H., Computational methods in drug design: Modeling G protein‐coupled receptor monomers, dimers, and oligomers. AAPS J. 2006, 8, E322–E336.
34. 34 Zhang, Y., Devries, M. E., Skolnick, J., Structure modeling of all identified G protein‐coupled receptors in the human genome. PLoS Comput. Biol. 2006, 2, e13.
35. 35 Evers, A., Hessler, G., Matter, H., Klabunde, T., Virtual screening of biogenic amine‐binding G‐protein coupled receptors: Comparative evaluation of protein‐ and ligandbased virtual screening protocols. J. Med. Chem. 2005, 48, 5448–5465.
36. 36 Shacham, S., Marantz, Y., Bar‐Haim, S., Kalid, O. et al., PREDICT modeling and in‐silico screening for G‐protein coupled receptors. Proteins 2004, 57, 51–86.
37. 37 Bissantz, C., Logean, A., Rognan, D., High‐throughput modeling of human G‐protein coupled receptors: Amino acid sequence alignment, three‐dimensional model building, and receptor library screening. J. Chem. Inf. Comput. Sci. 2004, 44, 1162–1176.
38. 38 Cavasotto, C. N., Orry, A. J., Abagyan, R. A., Structure‐based identification of binding sites, native ligands and potential inhibitors for G‐protein coupled receptors. Proteins 2003, 51, 423–433.
39. 39 Koshland, D. E. Jr., The lock‐and‐key principle and the induced‐fit theory. Angew. Chem. Int. Ed. Engl. 1994, 33, 2475–2478.
40. 40 Arkin, M. R., Wells, J. A., Small‐molecule inhibitors of protein‐protein interactions: Progressing towards the dream. Nat. Rev. Drug. Discov. 2004, 3, 301–317.
41. 41 Liu, Y., Gray, N. S., Rational design of inhibitors that bind to inactive kinase conformations. Nat. Chem. Biol. 2006, 2, 358–364.
42. 42 Gutteridge, A., Thornton, J., Conformational changes observed in enzyme crystal structures upon substrate binding. J. Mol. Biol. 2005, 346, 21–28.
43. 43 Davis, A. M., Teague, S. J., Hydrogen bonding, hydrophobic interactions, and failure of the rigid receptor hypothesis. Angew. Chem. Int. Ed. 1999, 38, 736–749.
44. 44 Thomas, M. P., McInnes, C., Fischer, P. M., Protein structures in virtual screening: A case study with CDK2. J. Med. Chem. 2006, 49, 92–104.
45. 45 Barril, X., Morley, S. D., Unveiling the full potential of flexible receptor docking using multiple crystallographic structures. J. Med. Chem. 2005, 48, 4432–4443.
46. 46 Rabal, O., Schneider, G., Borrell, J. I., Teixido, J., Structurebased virtual screening of FGFR inhibitors: Cross‐decoys and induced‐fit effect. BioDrugs 2007, 21, 31–45.
47. 47 McGovern, S. L., Shoichet, B. K., Information decay in molecular docking screens against holo, apo, and modeled conformations of enzymes. J. Med. Chem. 2003, 46, 2895–2907.
48. 48 Radestock, S., Bohm, M., Gohlke, H., Improving binding mode predictions by docking into protein‐specifically adapted potential fields. J. Med. Chem. 2005, 48, 5466–5479.
49. 49 Meagher, K. L., Carlson, H. A., Incorporating protein flexibility in structure‐based drug discovery: Using HIV‐1 protease as a test case. J. Am. Chem. Soc. 2004, 126, 13276–13281.
50. 50 Keizers, P. H., de Graaf, C., de Kanter, F. J., Oostenbrink, C. et al., Metabolic regio‐ and stereoselectivity of cytochrome P450 2D6 towards 3, 4‐methylenedioxy‐N‐alkylamphetamines: In silico predictions and experimental validation. J. Med. Chem. 2005, 48, 6117–6127.
51. 51 Alonso, H., Bliznyuk, A. A., Gready, J. E., Combining docking and molecular dynamic simulations in drug design. Med. Res. Rev. 2006, 26, 531–568.
52. 52 Corbeil, C. R., Englebienne, P., Moitessier, N., Docking ligands into flexible and solvated macromolecules. 1. Development and validation of FITTED 1.0. J. Chem. Inf. Model. 2007, 47, 435–449.
53. 53 Moitessier, N., Therrien, E., Hanessian, S., A method for induced‐fit docking, scoring, and ranking of flexible ligands. Application to peptidic and pseudopeptidic b‐secretase (BACE 1) inhibitors. J. Med. Chem. 2006, 49, 5885–5894.
54. 54 Sherman, W., Beard, H. S., Farid, R., Use of an induced fit receptor structure in virtual screening. Chem. Biol. Drug Des. 2006, 67, 83–84.
55. 55 Mizutani, M. Y., Takamatsu, Y., Ichinose, T., Nakamura, K. et al., Effective handling of induced‐fit motion in flexible docking. Proteins 2006, 63, 878–891.
56. 56 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.
57. 57 Williams, D. H., Bardsley, B., Estimating binding constants. The hydrophobic effect and cooperativity. Perspect. Drug Discov. Des. 1999, 17, 43–59.
58. 58 Houk, K. N., Leach, A. G., Kim, S. P., Zhang, X., Binding affinities of host‐guest, protein‐ligand, and protein‐transition‐state complexes. Angew. Chem. Int. Ed. 2003, 42, 4872–4897.
59. 59 Gilson, M. K., Zhou, H. X., Calculation of protein‐ligand binding affinities. Annu. Rev. Biophys. Biomol. Struct. 2007.
60. 60 Perozzo, R., Folkers, G., Scapozza, L., Thermodynamics of protein‐ligand interactions: History, presence, and future aspects. J. Recept. Signal. Transduct. Res. 2004, 24, 1–52.
61. 61 Helms, V., Wade, R. C., Computational alchemy to calculate absolute protein‐ligand binding free energy. J. Am. Chem. Soc. 1998, 120, 2710–2713.
62. 62 Weis, A., Katebzadeh, K., Soderhjelm, P., Nilsson, I. et al., Ligand affinities predicted with the MM/PBSA method: Dependence on the simulation method and the force field. J. Med. Chem. 2006, 49, 6596–6606.
63. 63 Vorobjev, Y. N., Hermans, J., ES/IS: Estimation of conformational free energy by combining dynamics simulations with explicit solvent with an implicit solvent continuum model. Biophys. Chem. 1999, 78, 195–205.
64. 64 Aqvist, J., Marelius, J., The linear interaction energy method for predicting ligand binding free energies. Comb. Chem. High Throughput Screen. 2001, 4, 613–626.
65. 65 Brooijmans, N., Kuntz, I. D., Molecular recognition and docking algorithms. Annu. Rev. Biophys. Biomol. Struct. 2003, 32, 335–373.
66. 66 Brown, S. P., Muchmore, S. W., High‐throughput calculation of protein‐ligand binding affinities: Modification and adaptation of the MM‐PBSA protocol to enterprise grid computing. J. Chem. Inf. Model. 2006, 46, 999–1005.
67. 67 Foloppe, N., Hubbard, R., Towards predictive ligand design with free‐energy based computational methods? Curr. Med. Chem. 2006, 13, 3583–3608.
68. 68 Case, D. A., Cheatham, T. E. III, Darden, T., Gohlke, H. et al., The Amber biomolecular simulation programs. J. Comput. Chem. 2005, 26, 1668–1688.
69. 69 Muegge, I., Martin, Y. C., A general and fast scoring function for protein‐ligand interactions: A simplified potential approach. J. Med. Chem. 1999, 42, 791–804.
70. 70 Wang, R., Lu, Y., Wang, S., Comparative evaluation of 11 scoring functions for molecular docking. J. Med. Chem. 2003, 46, 2287–2303.
71. 71 Ferrara, P., Gohlke, H., Price, D. J., Klebe, G. et al., Assessing scoring functions for protein‐ligand interactions. J. Med. Chem. 2004, 47, 3032–3047.
72. 72 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.
73. 73 Teramoto, R., Fukunishi, H., Supervised consensus scoring for docking and virtual screening. J. Chem. Inf. Model. 2007, 47, 526–534.
74. 74 Mpamhanga, C. P., Chen, B., McLay, I. M., Ormsby, D. L. et al., Retrospective docking study of PDE4B ligands and an analysis of the behavior of selected scoring functions. J. Chem. Inf. Model. 2005, 45, 1061–1074.
75. 75 Warren, G. L., Andrews, C. W., Capelli, A. M., Clarke, B. et al., A critical assessment of docking programs and scoring functions. J. Med. Chem. 2006, 49, 5912–5931.
76. 76 Leach, A. R., Shoichet, B. K., Peishoff, C. E., Prediction of protein‐ligand interactions. Docking and scoring: Successes and gaps. J. Med. Chem. 2006, 49, 5851–5855.
77. 77 Singh, P., Mhaka, A. M., Christensen, S. B., Gray, J. J. et al., Applying linear interaction energy method for rational design of noncompetitive allosteric inhibitors of the sarcoand endoplasmic reticulum calcium‐ATPase. J. Med. Chem. 2005, 48, 3005–3014.
78. 78 Kuhn, B., Gerber, P., Schulz‐Gasch, T., Stahl, M., Validation and use of the MM‐PBSA approach for drug discovery. J. Med. Chem. 2005, 48, 4040–4048.
79. 79 Nikitin, S., Zaitseva, N., Demina, O., Solovieva, V. et al., A very large diversity space of synthetically accessible compounds for use with drug design programs. J. Comput. Aided Mol. Des. 2005, 19, 47–63.
80. 80 Huwe, C. M., Synthetic library design. Drug Discov. Today 2006, 11, 763–76.
81. 81 Dominik, A., Computer‐assisted library design. Methods Princ. Med. Chem. 2006, 26, 559–613.
82. 82 Orry, A. J. W., Abagyan, R. A., Cavasotto, C. N., Structurebased development of target‐specific compound libraries. Drug Discov. Today 2006, 11, 261–266.
83. 83 Hergenrother, P. J., Obtaining and screening compound collections: A user's guide and a call to chemists. Curr. Opin. Chem. Biol. 2006, 10, 213–218.
84. 84 Norel, R., Wolfson, H. J., Nussinov, R., Small molecular recognition: Solid angles surface representation and molecular shape complementarity. Comb. Chem. High Throughput Screen. 1999, 2, 223–236.
85. 85 Luty, B. A., Wasserman, Z. R., Stouten, P. F. W., Hodge, C. N. et al., A molecular mechanics/grid method for evaluation of ligand‐receptor interactions. J. Comput. Chem. 1995, 16, 454–464.
86. 86 Brooijmans, N., Sharp, K. A., Kuntz, I. D., Stability of macromolecular complexes. Proteins 2002, 48, 645–653.
87. 87 Kitchen, D. B., Decornez, H., Furr, J. R., Bajorath, J., Docking and scoring in virtual screening for drug discovery: Methods and applications. Nat. Rev. Drug Discov. 2004, 3, 935–949.
88. 88 Lin, J. H., Perryman, A. L., Schames, J. R., McCammon, J. A., Computational drug design accommodating receptor flexibility: The relaxed complex scheme. J. Am. Chem. Soc. 2002, 124, 5632–5633.
89. 89 Pang, Y. P., Perola, E., Xu, K., Prendergast, F. G., EUDOC: A computer program for identification of drug interaction sites in macromolecules and drug leads from chemical databases. J. Comput. Chem. 2001, 22, 1750–1771.
90. 90 Sukuru, S. C. K., Crepin, T., Milev, Y., Marsh, L. C. et al., Discovering new classes of Brugia malayi asparaginyl‐tRNA synthetase inhibitors and relating specificity to conformational change. J. Comp. Aided Mol. Des. 2006, 20, 159–178.
91. 91 Cavasotto, C. N., Kovacs, J. A., Abagyan, R. A., Representing receptor flexibility in ligand docking through relevant normal modes. J. Am. Chem. Soc. 2005, 127, 9632–9640.
92. 92 Knegtel, R. M., Kuntz, I. D., Oshiro, C. M., Molecular docking to ensembles of protein structures. J. Mol. Biol. 1997, 266, 424–440.
93. 93 Osterberg, F., Morris, G. M., Sanner, M. F., Olson, A. J. et al., Automated docking to multiple target structures: Incorporation of protein mobility and structural water heterogeneity in AutoDock. Proteins 2002, 46, 34–40.
94. 94 Ferrari, A. M., Wei, B. Q., Costantino, L., Shoichet, B. K., Soft docking and multiple receptor conformations in virtual screening. J. Med. Chem. 2004, 47, 5076–5084.
95. 95 Zavodszky, M. I., Kuhn, L. A., Side‐chain flexibility in protein‐ligand binding: The minimal rotation hypothesis. Protein Sci. 2005, 14, 1104–1114.
96. 96 Chen, H., Lyne, P. D., Giordanetto, F., Lovell, T. et al., On evaluating molecular‐docking methods for pose prediction and enrichment factors. J. Chem. Inf. Model. 2006, 46, 401–415.
97. 97 Sato, H., Shewchuk, L. M., Tang, J., Prediction of multiple binding modes of the CDK2 inhibitors, anilinopyrazoles, using the automated docking programs GOLD, FlexX, and LigandFit: An evaluation of performance. J. Chem. Inf. Model. 2006, 46, 2552–2562.
98. 98 Cummings, M. D., DesJarlais, R. L., Gibbs, A. C., Mohan, V. et al., Comparison of automated docking programs as virtual screening tools. J. Med. Chem. 2005, 48, 962–976.
99. 99 Stoermer, M. J., Current status of virtual screening as analysed by target class. Med. Chem. 2006, 2, 89–11.
100. 100 Doman, T. N., McGovern, S. L., Witherbee, B. J., Kasten, T. P. et al., Molecular docking and high‐throughput screening for novel inhibitors of protein tyrosine phosphatase‐1B. J. Med. Chem. 2002, 45, 2213–2221.
101. 101 Polgar, T., Baki, A., Szendrei, G. I., Keseru, G. M., Comparative virtual and experimental high‐throughput screening for glycogen synthase kinase‐3b inhibitors. J. Med. Chem. 2005, 48, 7946–7959.
102. 102 Brenk, R., Irwin, J. J., Shoichet, B. K., Here be dragons: Docking and screening in an uncharted region of chemical space. J. Biomol. Screen. 2005, 10, 667–674.
103. 103 Davies, J. W., Glick, M., Jenkins, J. L., Streamlining lead discovery by aligning in silico and high‐throughput screening. Curr. Opin. Chem. Biol. 2006, 10, 343–351.
104. 104 Murray, C. W., Verdonk, M. L., The consequences of translational and rotational entropy lost by small molecules on binding to proteins. J. Comput. Aided Mol. Des. 2002, 16, 741–753.
105. 105 Honma, T., Recent advances in de novo design strategy for practical lead identification. Med. Res. Rev. 2003, 23, 606–632.
106. 106 Han, Q., Dominguez, C., Stouten, P. F., Park, J. M. et al., Design, synthesis, and biological evaluation of potent and selective amidino bicyclic factor Xa inhibitors. J. Med. Chem. 2000, 43, 4398–4415.
107. 107 Ali, M. A., Bhogal, N., Findlay, J. B., Fishwick, C. W., The first de novo‐designed antagonists of the human NK(2) receptor. J. Med. Chem. 2005, 48, 5655–5658.
108. 108 Hajduk, P. J., Fragment‐based drug design: How big is too big? J. Med. Chem. 2006, 49, 6972–6976.
109. 109 Hajduk, P. J., Greer, J., A decade of fragment‐based drug design: Strategic advances and lessons learned. Nat. Rev. Drug Discov. 2007, 6, 211–219.
110. 110 Hartshorn, M. J., Murray, C. W., Cleasby, A., Frederickson, M. et al., Fragment‐based lead discovery using X‐ray crystallography. J. Med. Chem. 2005, 48, 403–413.
111. 111 Leach, A. R., Hann, M. M., Burrows, J. N., Griffen, E. J., Fragment screening: An introduction. Mol. BioSyst. 2006, 2, 429–446.
112. 112 Zartler, E. R., Shapiro, M. J., Fragonomics: Fragment‐based drug discovery. Curr. Opin. Chem. Biol. 2005, 9, 366–370.
113. 113 Fischer, P. M., Protein‐protein interactions in drug discovery. Drug Des. Rev. Online 2005, 2, 179–207.
114. 114 Ripka, A. S., Rich, D. H., Peptidomimetic design. Curr. Opin. Chem. Biol. 1998, 2, 441–452.
115. 115 Fry, D., Sun, H., Utilizing peptide structures as keys for unlocking challenging targets. Mini Rev. Med. Chem. 2006, 6, 979–987.
116. 116 Wermuth, C. G., Selective optimization of side activities: Another way for drug discovery. J. Med. Chem. 2004, 47, 1303–1314.
117. 117 Bender, A., Glen, R. C., Molecular similarity: A key technique in molecular informatics. Org. Biomol. Chem. 2004, 2, 3204–3218.
118. 118 Kubinyi, H., Similarity and dissimilarity: A medicinal chemist's view. Perspect. Drug Discov. Des. 1998, 11, 225–252.
119. 119 Kier, L. B., Hall, L. H., Bioisosterism: Quantitation of structure and property effects. Chem. Biodivers. 2004, 1, 138–151.
120. 120 Eckert, H., Bajorath, J., Molecular similarity analysis in virtual screening: Foundations, limitations and novel approaches. Drug Discov. Today 2007, 12, 225–23.
121. 121 Bostrom, J., Hogner, A., Schmitt, S., Do structurally similar ligands bind in a similar fashion? J. Med. Chem. 2006, 49, 6716–6725.
122. 122 Pickett, S. D., Mason, J. S., McLay, I. M., Diversity profiling and design using 3D pharmacophores: Pharmacophorederived queries (PDQ). J. Chem. Inf. Comp. Sci. 1996, 36, 1214–1223.
123. 123 Mason, J. S., Morize, I., Menard, P. R., Cheney, D. L. et al., New 4‐point pharmacophore method for molecular similarity and diversity applications: Overview of the method and applications, including a novel approach to the design of combinatorial libraries containing privileged substructures. J. Med. Chem. 1999, 42, 3251–3264.
124. 124 Abrahamian, E., Fox, P. C., Nrum, L., Christensen, I. T. et al., Efficient generation, storage, and manipulation of fully flexible pharmacophore multiplets and their use in 3‐D similarity searching. J. Chem. Inf. Comp. Sci. 2003, 43, 458–468.
125. 125 Mestres, J., Rohrer, D. C., Maggiora, G. M., MIMIC: A molecular‐field matching program. Exploiting applicability of molecular similarity approaches. J. Comput. Chem. 1997, 18, 934–954.
126. 126 Schuur, J. H., Selzer, P., Gasteiger, J., The coding of the three‐dimensional structure of molecules by molecular transforms and its application to structure‐spectra correlations and studies of biological activity. J. Chem. Inf. Model. 1996, 36, 334–344.
127. 127 Ahlstrom, M. M., Ridderstrom, M., Luthman, K., Zamora, I., Virtual screening and scaffold hopping based on GRID molecular interaction fields. J. Chem. Inf. Model. 2005, 45, 1313–1323.
128. 128 Fang, X., Wang, S., A Web‐based 3D‐database pharmacophore searching tool for drug discovery. J. Chem. Inf. Comp. Sci. 2002, 42, 192–198.
129. 129 Karelson, M., Lobanov, V. S., Katritzky, A. R., Quantumchemical descriptors in QSAR/QSPR studies. Chem. Rev. 1996, 96, 1027–1044.
130. 130 Livingstone, D. J., The characterization of chemical structures using molecular properties. A survey. J. Chem. Inf. Model. 2000, 40, 195–209.
131. 131 Pearlman, R. S., Smith, K. M., Metric validation and the receptor‐relevant subspace concept. J. Chem. Inf. Model. 1999, 39, 28–35.
132. 132 Hert, J., Willett, P., Wilton, D. J., Acklin, P. et al., Comparison of topological descriptors for similarity‐based virtual screening using multiple bioactive reference structures. Org. Biomol. Chem. 2004, 2, 3256–3266.
133. 133 Butina, D., Unsupervised data base clustering based on Daylight's fingerprint and Tanimoto similarity: A fast and automated way to cluster small and large data sets. J. Chem. Inf. Comp. Sci. 1999, 39, 747–750.
134. 134 Durant, J. L., Leland, B. A., Henry, D. R., Nourse, J. G., Reoptimization of MDL keys for use in drug discovery. J. Chem. Inf. Comp. Sci. 2002, 42, 1273–1280.
135. 135 Willett, P., Barnard, J. M., Downs, G. M., Chemical similarity searching. J. Chem. Inf. Model. 1998, 38, 983–996.
136. 136 Willett, P., Searching techniques for databases of two‐ and three‐dimensional chemical structures. J. Med. Chem. 2005, 48, 4183–4199.
137. 137 Sheridan, R. P., Kearsley, S. K., Why do we need so many chemical similarity search methods? Drug discov. today 2002, 7, 903–911.
138. 138 Martin, Y. C., Kofron, J. L., Traphagen, L. M., Do structurally similar molecules have similar biological activity? J. Med. Chem. 2002, 45, 4350–4358.
139. 139 Zhang, Q., Muegge, I., Scaffold hopping through virtual screening using 2D and 3D similarity descriptors: Ranking, voting, and consensus scoring. J. Med. Chem. 2006, 49, 1536–1548.
140. 140 Williams, C., Reverse fingerprinting, similarity searching by group fusion and fingerprint bit importance. Mol. Divers. 2006, 10, 311–332.
141. 141 Maggiora, G. M., On outliers and activity cliffs‐why QSAR often disappoints. J. Chem. Inf. Model. 2006, 46, 1535–1535.
142. 142 Hansch, C., Klein, T. E., Quantitative structure‐activity relationships and molecular graphics in evaluation of enzyme‐ligand interactions. Methods Enzymol. 1991, 202, 512–543.
143. 143 Sprous, D. G., Zhang, J., Zhang, L., Wang, Z. et al., Kinase inhibitor recognition by use of a multivariable QSAR model. J. Mol. Graph. Model. 2006, 24, 278–295.
144. 144 Winkler, D. A., The role of quantitative structure‐activity relationships (QSAR) in biomolecular discovery. Brief. Bioinform. 2002, 3, 73–86.
145. 145 Yang, G. F., Huang, X., Development of quantitative structure‐activity relationships and its application in rational drug design. Curr. Pharm. Des. 2006, 12, 4601–4611.
146. 146 Cramer, R. D. III, Patterson, D. E., Bunce, J. D., Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J. Am. Chem. Soc. 1988, 110, 5959–5967.
147. 147 Böhm, M., Stürzebecher, J., Klebe, G., Three‐dimensional quantitative structure‐activity relationship analyses using comparative molecular field analysis and comparative molecular similarity indices analysis to elucidate selectivity differences of inhibitors binding to trypsin, thrombin, and factor Xa. J. Med. Chem. 1999, 42, 458–477.
148. 148 Klebe, G., Abraham, U., Mietzner, T., Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J. Med. Chem. 1994, 37, 4130–4146.
149. 149 Klebe, G., Abraham, U., Comparative molecular similarity index analysis (CoMSIA) to study hydrogen‐bonding properties and to score combinatorial libraries. J. Comput. Aided Mol. Des. 1999, 13, 1–10.
150. 150 Mor, M., Rivara, S., Lodola, A., Lorenzi, S. et al., Application of 3D‐QSAR in the rational design of receptor ligands and enzyme inhibitors. Chem. Biodivers. 2005, 2, 1438–1451.
151. 151 Klabunde, T., Evers, A., GPCR antitarget modeling: Pharmacophore models for biogenic amine binding GPCRs to avoid GPCR‐mediated side effects. Chembiochem 2005, 6, 876–889.
152. 152 Li, Y., Harte, W. E., A review of molecular modeling approaches to pharmacophore models and structure‐activity relationships of ion channel modulators in CNS. Curr. Pharm. Des. 2002, 8, 99–110.
153. 153 Liu, H., Gao, Z. B., Yao, Z., Zheng, S. et al., Discovering potassium channel blockers from synthetic compound database by using structure‐based virtual screening in conjunction with electrophysiological assay. J. Med. Chem. 2007, 50, 83–93.
154. 154 Birch, P. J., Dekker, L. V., James, I. F., Southan, A. et al., Strategies to identify ion channel modulators: Current and novel approaches to target neuropathic pain. Drug Discov. Today 2004, 9, 410–418.
155. 155 Grueneberg, S., A QSAR model derived from a homology model: A strategy to include structural information in ligand‐based design. QSAR Comb. Sci. 2005, 24, 517–526.
156. 156 Heritage, T. W., Lowis, D. R., Molecular hologram QSAR. ACS Symp. Ser. 1999, 719, 212–225.
157. 157 Lowis, D. R., HQSAR: A new, highly predictive QSAR technique. Tripos Technical Notes 1997, 1, 1–17.
158. 158 Waller, C. L., A comparative QSAR study using CoMFA, HQSAR, and FRED/SKEYS paradigms for estrogen receptor binding affinities of structurally diverse compounds. J. Chem. Inf. Comput. Sci. 2004, 44, 758–765.
159. 159 Hopfinger, A. J., Wang, S., Tokarski, J. S., Jin, B. et al., Construction of 3D‐QSAR models using the 4D‐QSAR analysis formalism. J. Am. Chem. Soc. 1997, 119, 10509–10524.
160. 160 Liu, J., Pan, D., Tseng, Y., Hopfinger, A. J., 4D‐QSAR analysis of a series of antifungal P450 inhibitors and 3D‐pharmacophore comparisons as a function of alignment. J. Chem. Inf. Comp. Sci. 2003, 43, 2170–2179.
161. 161 Vedani, A., Briem, H., Dobler, M., Dollinger, H. et al., Multiple‐conformation and protonation‐state representation in 4D‐QSAR: The neurokinin‐1 receptor system. J. Med. Chem. 2000, 43, 4416–4427.
162. 162 Vedani, A., Dobler, M., 5D‐QSAR: The key for simulating induced fit? J. Med. Chem. 2002, 45, 2139–2149.
163. 163 Vedani, A., Dobler, M., Lill, M. A., Combining protein modeling and 6D‐QSAR. Simulating the binding of structurally diverse ligands to the estrogen receptor. J. Med. Chem. 2005, 48, 3700–3703.
164. 164 Lill, M. A., Vedani, A., Combining 4D pharmacophore generation and multidimensional QSAR: Modeling ligand binding to the bradykinin B2 receptor. J. Chem. Inf. Model. 2006, 46, 2135–2145.
165. 165 Krasowski, M. D., Hong, X., Hopfinger, A. J., Harrison, N. L., 4D‐QSAR analysis of a set of propofol analogues: Mapping binding sites for an anesthetic phenol on the GABAA receptor. J. Med. Chem. 2002, 45, 3210–3221.
166. 166 Hong, X., Hopfinger, A. J., 3D‐Pharmacophores of flavonoid binding at the benzodiazepine GABAA receptor site using 4D‐QSAR analysis. J. Chem. Inf. Comp. Sci. 2003, 43, 324–336.
167. 167 Santos‐Filho, O. A., Hopfinger, A. J., The 4D‐QSAR paradigm: Application to a novel set of nonpeptidic HIV protease inhibitors. QSAR 2002, 21, 369–381.
168. 168 Hopfinger, A. J., Reaka, A., Venkatarangan, P., Duca, J. S. et al., Construction of a virtual high throughput screen by 4D‐QSAR analysis: Application to a combinatorial library of glucose inhibitors of glycogen phosphorylase b. J. Chem. Inf. Comp. Sci. 1999, 39, 1151–1160.
169. 169 Liu, G., Small molecule antagonists of the LFA‐1/ICAM‐1 interaction as potential therapeutic agents. Expert Opin. Ther. Pat. 2001, 11, 1383–1393.
170. 170 Shimaoka, M., Xiao, T., Liu, J.‐H., Yang, Y. et al., Structures of the alpha L I domain and its complex with ICAM‐1 reveal a shape‐shifting pathway for integrin regulation. Cell 2003, 112, 99–111.
171. 171 Shimaoka, M., Lu, C., Palframan, R. T., Von Andrian, U. H. et al., Reversibly locking a protein fold in an active conformation with a disulfide bond: Integrin (L I domains) with high affinity and antagonist activity in vivo. Proc. Natl. Acad. Sci. USA 2001, 98, 6009–6014.
172. 172 Qu, A., Leahy, D. J., Crystal structure of the I‐domain from the CD11a/CD18 (LFA‐1, alpha L beta 2) integrin. Proc. Natl. Acad. Sci. USA 1995, 92, 10277–10281.
173. 173 Kallen, J., Welzenbach, K., Ramage, P., Geyl, D. et al., Structural basis for LFA‐1 inhibition upon lovastatin binding to the CD11a I‐domain. J. Mol. Biol. 1999, 292, 1–9.
174. 174 Weitz‐Schmidt, G., Welzenbach, K., Dawson, J., Kallen, J., Improved lymphocyte function‐associated antigen‐1 (LFA‐1) inhibition by statin derivatives: Molecular basis determined by x‐ray analysis and monitoring of LFA‐1 conformational changes in vitro and ex vivo. J. Biol. Chem. 2004, 279, 46764–46771.
175. 175 Crump, M. P., Ceska, T. A., Spyracopoulos, L., Henry, A. et al., Structure of an allosteric inhibitor of LFA‐1 bound to the I‐domain studied by crystallography, NMR, and calorimetry. Biochemistry 2004, 43, 2394–2404.
176. 176 Wattanasin, S., Kallen, J., Myers, S., Guo, Q. et al., 1,4‐Diazepane‐2,5‐diones as novel inhibitors of LFA‐1. Bioorg. Med. Chem. Lett. 2005, 15, 1217–1220.
177. 177 Potin, D., Launay, M., Monatlik, F., Malabre, P. et al., Discovery and development of 5‐[(5S,9R)‐9‐(4‐cyanophenyl)‐3‐(3,5‐dichlorophenyl)‐1‐methyl‐2,4‐dioxo‐1,3,7‐triazaspiro [4.4]non‐7‐yl‐methyl]‐3‐thiophenecarboxylic acid (BMS‐587101)–A small molecule antagonist leukocyte function associated antigen‐1. J. Med. Chem. 2006, 49, 6946–6949.
178. 178 Weber, P. C., Ohlendorf, D. H., Wendoloski, J. J., Salemme, F. R., Structural origins of high‐affinity biotin binding to streptavidin. Science 1989, 243, 85–88.
179. 179 Thanos, C. D., Randal, M., Wells, J. A., Potent small‐molecule binding to a dynamic hot spot on IL‐2. J. Am. Chem. Soc. 2003, 125, 15280–15281.
180. 180 Howard, N., Abell, C., Blakemore, W., Chessari, G. et al., Application of fragment screening and fragment linking to the discovery of novel thrombin inhibitors. J. Med. Chem. 2006, 49, 1346–1355.