Recent trends and applications in 3D virtual screening

Combinatorial Chemistry and High Throughput Screening

Volumn 15, Issue 9, 2012, Pages 749-769

  Ghemtio, Leo ^a,b   Pérez Nueno, Violeta I ^a,e   Leroux, Vincent ^c   Asses, Yasmine ^a,e   Souchet, Michel ^d   Mavridis, Lazaros ^a,e   Maigret, Bernard ^a,e   Ritchie, David W ^a,e  

^a UNIVERSITÉ DE LORRAINE   (France)

^b UNIVERSITY OF HELSINKI   (Finland)

^c UNIVERSITÉ DE BORDEAUX   (France)

^d Harmonic Pharma   (France)

^e LORIA   (France)

Author keywords

3D shape matching; 3D QSAR; Chemical libraries; Chemical spaces; Computer aided drug design; Conformational flexibility; Docking; Knowledge based drug design; Ligand based drug design; Molecular dynamics; Pharmacophores; Structurebased drug design

Indexed keywords

BETA ADRENERGIC RECEPTOR BLOCKING AGENT; CALCIUM CHANNEL BLOCKING AGENT; CHEMOKINE RECEPTOR CCR5 ANTAGONIST; CHEMOKINE RECEPTOR CXCR4 ANTAGONIST; HUMAN IMMUNODEFICIENCY VIRUS FUSION INHIBITOR; HYDROXYMETHYLGLUTARYL COENZYME A REDUCTASE INHIBITOR; NATURAL PRODUCT; NEW DRUG; TRICYCLIC ANTIDEPRESSANT AGENT;

ARTICLE; COMPUTER AIDED DESIGN; CONFORMATIONAL TRANSITION; COVALENT BOND; DRUG BINDING SITE; DRUG DESIGN; DRUG INDUSTRY; DRUG SCREENING; DRUG SYNTHESIS; DYNAMICS; LIGAND BINDING; MOLECULAR DOCKING; MOLECULAR MODEL; PHARMACOPHORE; PRIORITY JOURNAL; PROTEIN BINDING; PROTEIN CONFORMATION; QUANTITATIVE STRUCTURE ACTIVITY RELATION; THREE DIMENSIONAL IMAGING;

DRUG DISCOVERY; HIGH-THROUGHPUT SCREENING ASSAYS; HIV; HUMANS; MOLECULAR STRUCTURE; ORPHAN NUCLEAR RECEPTORS; RECEPTORS, CCR5; RECEPTORS, CXCR4;

EID: 84870463721     PISSN: 13862073     EISSN: 18755402     Source Type: Journal    
DOI: 10.2174/138620712803519707     Document Type: Article

References (289)

1. Seifert, M. M. J.; Lang, M. Essential factors for successful virtual screening. Mini-Rev Med. Chem., 2008, 8(1), 63-72. (Pubitemid 351320931)
2. Matter, H.; Sotriffer, C. Applications and Success Stories in Virtual Screening. In Virtual Screening; Wiley-VCH Verlag GmbH & Co. KGaA: 2011; pp 319-358.
3. Clark, D. D. What has virtual screening ever done for drug discovery? Exp. Opin. Drug Discov., 2008, 3(8), 841-851.
4. Shoichet, B. B. Virtual screening of chemical libraries. Nature, 2004, 432, 862-865.
5. Bajorath, J. Integration of virtual and high-throughput screening. Nat. Rev. Drug Discov., 2002, 1, 882-894. (Pubitemid 37361583)
6. Mestres, J. Virtual screening: a real screening complement to highthroughput screening. Biochem. Soc. Trans., 2002, 30, 797-799. (Pubitemid 35001661)
7. Phatak, S. S.; Stephan, C. C.; Cavasotto, C. C. High-throughput and in silico screenings in drug discovery. Exp. Opin. Drug Discov., 2009, 4, 947-959.
8. Davies, J. W.; Glick, M.; Jenkins, J. J. Streamlining lead discovery by aligning in silico and high-throughput screening. Curr. Opin. Chem. Biol., 2006, 10, 343-351. (Pubitemid 44080652)
9. Hawkins, P. P. D.; Skillman, A. G.; Nicholls, A. Comparison of shape-matching and docking as virtual screening tools. J. Med. Chem., 2007, 50, 74-82. (Pubitemid 46105500)
10. McGaughey, G. B.; Sheridan, R. P.; Bayly, C. I.; Culberson, J. C.; 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, 1504-1519. (Pubitemid 47210053)
11. Klebe, G. Virtual ligand screening: strategies, perspectives and limitations. Drug Discov. Today, 2006, 11, 580-594.
12. Cramer, R. R. Rethinking 3D-QSAR. J. Comput. Aided Molecule Des., 2011, 25, 197-201.
13. De Benedetti, P. G.; Fanelli, F. Computational quantum chemistry and adaptive ligand modeling in mechanistic QSAR. Drug Discov. Today, 2010, 15, 859-866.
14. Verma, J.; Khedkar, V. M.; Coutinho, E. E. 3D-QSAR in drug design - a review. Curr. Top. Med. Chem., 2010, 10, 95-115.
15. Geppert, H.; Vogt, M.; Bajorath, J. Current trends in ligand-based virtual screening: molecular representations, data mining methods, new application areas, and performance evaluation. J. Chem. Inf. Model., 2010, 50, 205-216.
16. Sukumar, N.; Das, S. Current trends in virtual high-throughput screening using ligand-based and structure-based methods. Comb. Chem. High Through. Screen., 2011, 14, 872-888.
17. Henrich, S.; Salo-Ahen, O. O. H.; Huang, B.; Rippmann, F. F.; Cruciani, G.; Wade, R. R. Computational approaches to identifying and characterizing protein binding sites for ligand design. J. Mol. Recognit., 2010, 23, 209-219.
18. Huang, S.-Y.; Zou, X. Advances and challenges in protein-ligand docking. Int. J. Mol. Sci., 2010, 11, 3016-3034.
19. Rognan, D. Structure-based approaches to target fishing and ligand profiling. Mol. Inf., 2010, 29, 176-187.
20. 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. (Pubitemid 39529931)
21. Ghosh, S.; Nie, A.; An, J.; Huang, Z. Structure-based virtual screening of chemical libraries for drug discovery. Curr. Opin. Chem. Biol., 2006, 10, 194-202. (Pubitemid 43815778)
22. Kellenberger, E.; Foata, N.; Rognan, D. Ranking targets in structure-based virtual screening of three-dimensional protein libraries: methods and problems. J. Chem. Inf. Model., 2008, 48, 1014-1025.
23. Kuntz, I. I. Structure-based strategies for drug design and discovery. Science, 1992, 257, 1078-1082.
24. Tanrikulu, Y.; Schneider, G. Pseudoreceptor models in drug design: bridging ligand-and receptor-based virtual screening. Nat. Rev. Drug Discov., 2008, 7, 667-677.
25. Clark, R. R. Prospective ligand-and target-based 3D QSAR: state of the art 2008. Curr. Top. Med. Chem., 2009, 9(9), 791-810.
26. Koutsoukas, A.; Simms, B.; Kirchmair, J.; Bond, P. J.; Whitmore, A. V.; Zimmer, S.; Young, M. P.; Jenkins, J. L.; Glick, M.; Glen, R. C.; Bender, A. From in silico target prediction to multi-target drug design: Current databases, methods and applications. J. Proteomics, 2011, 74, 2554-2574.
27. Omagari, K.; Mitomo, D.; Kubota, S.; Nakamura, H.; Fukunishi, Y. A method to enhance the hit ratio by a combination of structurebased drug screening and ligand-based screening. Adv. Appl. Bioinform. Chem., 2008, 1, 19-28.
28. Villoutreix, B. O.; Renault, N.; Lagorce, D.; Sperandio, O.; Montes, M.; Miteva, M. M. Free resources to assist structure-based virtual ligand screening experiments. Curr. Protein Pept. Sci., 2007, 8, 381-411. (Pubitemid 47321574)
29. Varnek, A.; Tropsha, A., Chemoinformatics approaches to virtual screening. Royal Society of Chemistry: Cambridge, UK, 2008.
30. Alvarez, J. C.; Shoichet, B. K., Virtual screening in drug discovery. Taylor & Francis: Boca Raton, FL, 2005.
31. Oprea, T. I., Chemoinformatics in drug discovery. Wiley-VCH: 2005.
32. Polgár, T.; Keser, G. G. Integration of virtual and highthroughput screening in lead discovery settings. Comb. Chem. High Throughput Screen., 2011, 14, 889-897.
33. Kirkpatrick, P.; Ellis, C. Chemical space. Nature, 2004, 432(7019), 823-823.
34. Lipkus, A. H.; Yuan, Q.; Lucas, K. A.; Funk, S. A.; Bartlett III, W. F.; Schenck, R. J.; Trippe, A. A. Structural diversity of organic chemistry. A scaffold analysis of the CAS registry. J. Org. Chem., 2008, 73, 4443-4451. (Pubitemid 351842458)
35. Dobson, C. C. Chemical space and biology. Nature, 2004, 432, 824-828.
36. Lameijer, E.-W.; Kok, J. N.; Bäck, T.; Ijzerman, A. A. Mining a chemical database for fragment co-occurrence: discovery of "chemical clichés". J. Chem. Inf. Model., 2006, 46, 553-562.
37. Leeson, P. D.; St-Gallay, S. S. The influence of the 'organizational factor' on compound quality in drug discovery. Nat. Rev. Drug Discov., 2011, 10, 749-765.
38. Munos, B. Lessons from 60 years of pharmaceutical innovation. Nat. Rev. Drug Discov., 2009, 8, 959-968.
39. Schmid, E. F.; Smith, D. D. Is declining innovation in the pharmaceutical industry a myth? Drug Discov. Today, 2005, 10, 1031-1039. (Pubitemid 41074141)
40. Williams, R. Discontinued drugs in 2008: oncology drugs. Exp. Opin. Invest. Drugs, 2009, 18, 1581-1594.
41. Harrison, C. The patent cliff steepens. Nat. Rev. Drug Discov., 2011, 10, 12-13.
42. LaMattina, J. J. The impact of mergers on pharmaceutical R&D. Nat. Rev. Drug Discov., 2011, 10, 559-560.
43. Tralau-Stewart, C. J.; Wyatt, C. A.; Kleyn, D. E.; Ayad, A. Drug discovery: new models for industry-academic partnerships. Drug Discov. Today, 2009, 14, 95-101.
44. Triggle, D. D. The chemist as astronaut: searching for biologically useful space in the chemical universe. Biochem. Pharmacol., 2009, 78, 217-223.
45. Irwin, J. J. Staring off into chemical space. Nat. Chem. Biol., 2009, 5, 536-537.
46. Lipinski, C. A.; Hopkins, A. Navigating chemical space for biology and medicine. Nature, 2004, 432, 855-861. (Pubitemid 40037141)
47. Congreve, M.; Carr, R.; Murray, C.; Jhoti, H. A "rule of three" for fragment-based drug discovery? Drug Discov. Today, 2003, 8, 876-877.
48. Reymond, J.-L.; van Deursen, R.; Blum, L. C.; Ruddigkeit, L. Chemical space as a source for new drugs. Medchemcomm, 2010, 1, 30-38.
49. Fink, T.; Bruggesser, H.; Reymond, J.-L. Virtual exploration of the small-molecule chemical universe below 160 Daltons. Angewandte Chemie, 2005, 44, 1504-1508. (Pubitemid 40460043)
50. Fink, T.; 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., 2007, 47, 342-353. (Pubitemid 46615939)
51. Feher, M.; Schmidt, J. J. Property distributions: differences between drugs, natural products, and molecules from combinatorial chemistry. J. Chem. Inf. Comput. Sci., 2003, 43, 218-227.
52. Valle, S.; Escher, E.; Lubell, W. D.; Kappel, J. C.; Fan, Y. C.; Lam, K. K. Application of the "Libraries from Libraries" concept to "One-bead One-compound" combinatorial chemistry. In Peptides for youth; Springer New York: 2009; Vol. 611, pp 21-22.
53. Wermuth, C. C. Similarity in drugs: reflections on analogue design. Drug Discov. Today, 2006, 11, 348-354.
54. Newman, D. J.; Cragg, G. G. Natural products as sources of new drugs over the last 25 years. J. Nat. Prod., 2007, 70, 461-477. (Pubitemid 46595760)
55. Clardy, J.; Walsh, C. Lessons from natural molecules. Nature, 2004, 432, 829-837. (Pubitemid 40037138)
56. Schreiber, S. S. Target-oriented and diversity-oriented organic synthesis in drug discovery. Science, 2000, 287, 1964-1969.
57. Morton, D.; Leach, S.; Cordier, C.; Warriner, S.; Nelson, A. Synthesis of natural-product-like molecules with over eighty distinct scaffolds. Angewandte Chemie, 2009, 48, 104-109.
58. Koch, M. A.; Schuffenhauer, A.; Scheck, M.; Wetzel, S.; Casaulta, M.; Odermatt, A.; Ertl, P.; Waldmann, H. Charting biologically relevant chemical space: a structural classification of natural products (SCONP). Proc. Natl Acad. Sci. USA, 2005, 102, 17272-17277. (Pubitemid 41740856)
59. Berman, H. M.; Battistuz, T.; Bhat, T. N.; Bluhm, W. F.; Bourne, P. E.; Buckhardt, K.; Feng, Z.; Gilliland, G. L.; Iype, L.; Jain, S.; Fagan, P.; Marvin, J.; Padilla, D.; Ravichandran, V.; Schneider, B.; Thanki, N.; Weissig, H.; Westbrook, J. D.; Zardecki, C. The Protein Data Bank. Acta Crystallographica D., 2002, 58, 899-907. (Pubitemid 34653017)
60. Moos, W. H.; Hurt, C. R.; Morales, G. G. Combinatorial chemistry: oh what a decade or two can do. Mol. Divers., 2009, 13, 241-245.
61. van Hijfte, L.; Marciniak, G.; Froloff, N. Combinatorial chemistry, automation and molecular diversity: new trends in the pharmaceutical industry. J. Chromatogr. B, 1999, 725, 3-15. (Pubitemid 30173217)
62. Bures, M. G.; Martin, Y. Y. Computational methods in molecular diversity and combinatorial chemistry. Curr. Opin. Chem. Biol., 1998, 2, 376-380. (Pubitemid 128423242)
63. Chuprina, A.; Lukin, O.; Demoiseaux, R.; Buzko, A.; Shivanyuk, A. Drug-and lead-likeness, target class, and molecular diversity analysis of 7.9 million commercially available organic compounds provided by 29 suppliers. J. Chem. Inf. Model., 2010, 50, 470-479.
64. Irwin, J. J.; Shoichet, B. B. ZINC - a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model., 2005, 45, 177-82. (Pubitemid 40736970)
65. Irwin, J. J. Using ZINC to acquire a virtual screening library. Curr. Protoc. Bioinformatics, 2008, Chapter 14, Unit 14 6.
66. Hibert, M. M. French/European academic compound library initiative. Drug Discov. Today, 2009, 14, 723-725.
67. Knox, C.; Law, V.; Jewison, T.; Liu, P.; Ly, S.; Frolkis, A.; Pon, A.; Banco, K.; Mak, C.; Neveu, V.; Djoumbou, Y.; Eisner, R.; Guo, A. C.; Wishart, D. D. DrugBank 3.0: a comprehensive resource for 'omics' research on drugs. Nucleic Acids Res., 2011, 39, D1035-D1041.
68. Koutsoukas, A.; Simms, B.; Kirchmair, J.; Bond, P. J.; Whitmore, A. V.; Zimmer, S.; Young, M. P.; Jenkins, J. L.; Glick, M.; Glen, R. C.; Bender, A. From in silico target prediction to multi-target drug design: current databases, methods and applications. J. Proteomics, 2011, 74(12), 2554-2574.
69. Koppen, H. Virtual screening - what does it give us? Curr. Opin. Drug Discov. Develop., 2009, 12(3), 397-407.
70. Caldwell, G. G. Compound optimization in early-and late-phase drug discovery: acceptable pharmacokinetic properties utilizing combined physicochemical, in vitro and in vivo screens. Curr. Opin. Drug Discov. Develop., 2000, 3, 30-41.
71. Raju, T. T. K. The Nobel chronicles. The Lancet, 2000, 356, 346.
72. DiMasi, J. A.; Faden, L. L. Competitiveness in follow-on drug R&D: a race or imitation? Nat. Rev. Drug Discov., 2011, 10, 23-27.
73. Sundar, S.; Jha, T. K.; Thakur, C. P.; Bhattacharya, S. K.; Rai, M. Oral miltefosine for the treatment of Indian visceral leishmaniasis. T. Roy. Soc. Trop. Med. H, 2006, 100 S, 26-33.
74. Wermuth, C. C. Selective optimization of side activities: another way for drug discovery. J. Med. Chem., 2004, 47, 1303-1314.
75. Congreve, M.; Chessari, G.; Tisi, D.; Woodhead, A. A. Recent developments in fragment-based drug discovery. J. Med. Chem., 2008, 51, 3661-3680. (Pubitemid 351956496)
76. 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. (Pubitemid 46344625)
77. Siegal, G.; Ab, E.; Schultz, J. Integration of fragment screening and library design. Drug Discov. Today, 2007, 12, 1032-1039. (Pubitemid 350180558)
78. Vieth, M.; Siegel, M. G.; Higgs, R. E.; Watson, I. A.; Robertson, D. H.; Savin, K. A.; Durst, G. L.; Hipskind, P. P. Characteristic physical properties and structural fragments of marketed oral drugs. J. Med. Chem., 2004, 47, 224-232. (Pubitemid 38040506)
79. Furlan, A.; Colombo, F.; Kover, A.; Issaly, N.; Tintori, C.; Angeli, L.; Leroux, V.; Letard, S.; Amat, M.; Asses, Y.; Maigret, B.; Dubreuil, P.; Botta, M.; Dono, R.; Bosch, J.; Piccolo, O.; Passarella, D.; Maina, F. Identification of new aminoacid amides containing the imidazo[2, 1-b]benzothiazol-2-ylphenyl moiety as inhibitors of tumorigenesis by oncogenic Met signaling. Eur. J. Med. Chem., 2012, 47(1), 239-254.
80. Auer, J.; Bajorath, J. Molecular similarity concepts and search calculations. In Methods in molecular biology vol. 453. Bioinformatics II: structure, function and applications; J. M. Keith, Ed. Humana Press: Totowa, NJ, 2008; pp 327-347.
81. Martin, Y. C.; Kofron, J. L.; Traphagen, L. L. Do structurally similar molecules have similar biological activity? J. Med. Chem., 2002, 45, 4350-4358. (Pubitemid 35025581)
82. Martin, Y. Y. Diverse viewpoints on computational aspects of molecular diversity. J. Comb. Chem., 2001, 3(3), 231-250.
83. Kirchmair, J.; Ristic, S.; Eder, K.; Markt, P.; Wolber, G.; Laggner, C.; Langer, T. Fast and efficient in silico 3D screening: toward maximum computational efficiency of pharmacophore-based and shape-based approaches. J. Chem. Inf. Model., 2007, 47, 2182-2196. (Pubitemid 350275084)
84. Lemmen, C.; Lengauer, T. Computational methods for the structural alignment of molecules. J. Comput. Aid. Mol. Des., 2000, 14, 215-332.
85. Xue, L.; Bajorath, J. Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Comb. Chem. High Throughput Screen., 2000, 3(5), 363-72.
86. Willett, P.; Barnard, J. M.; Downs, G. G. Chemical similarity searching. J. Chem. Inf. Comput. Sci., 1998, 38, 983-996. (Pubitemid 128591261)
87. Wolber, G.; Dornhofer, A. A.; Langer, T. Efficient overlay of small organic molecules using 3D pharmacophores. J. Comput. Aid. Mol. Des., 2006, 20, 773-788. (Pubitemid 46476946)
88. Mestres, J.; Veeneman, G. G. Identification of "latent hits" in compound screening collections. J. Med. Chem., 2003, 46, 3441-3444. (Pubitemid 36909817)
89. Brown, R. D.; Martin, Y. Y. Use of structure-activity data to compare structure-based clustering methods and descriptors to use in compound selection. J. Chem. Inf. Model., 1996, 36, 572-584. (Pubitemid 126539358)
90. Bender, A.; Jenkins, J. L.; Scheiber, J.; Sukuru, S. S. K.; Glick, M.; Davies, J. J. How similar are similarity searching methods? A principal component analysis of molecular descriptor space. J. Chem. Inf. Model., 2009, 49(1), 108-119.
91. Güner, O. F.; Hughes, D. W.; Dumont, L. L. An integrated approach to three-dimensional information management with MACCS-3D. J. Chem. Inf. Comput. Sci., 1991, 31, 408-414.
92. Cannon, E. O.; Nigsch, F.; Mitchell, J. J. O. A novel hybrid ultrafast shape descriptor method for use in virtual screening. Chem. Cent. J., 2008, 2.
93. Renner, S.; Fechner, U.; Schneider, G. Alignment-free pharmacophore patterns - A correlation-vector approach. In Pharmacophores and pharmacophore searches; Wiley-VCH Verlag GmbH & Co. KGaA: 2006; pp 49-79.
94. Nærum, L.; Nørskov-Lauritsen, L.; Olesen, P. P. Scaffold hopping and optimization towards libraries of glycogen synthase kinase-3 inhibitors. Bioorg. Med. Chem. Lett., 2002, 12, 1525-1528. (Pubitemid 34521927)
95. Tsunoyama, K.; Amini, A.; Sternberg, M. M. E.; Muggleton, S. S. Scaffold hopping in drug discovery using inductive logic programming. J. Chem. Inf. Model., 2008, 48, 949-957.
96. Smiesko, M.; Remko, M. Use of the pharmacophore concept in drug design. Ceska Slov Farm, 1999, 48(6), 247-251.
97. Patel, Y.; Gillet, V. J.; Bravi, G.; Leach, A. A. A comparison of the pharmacophore identification programs: Catalyst, DISCO and GASP. J. Compu. Aided Mol. Des., 2002, 16, 653-681. (Pubitemid 36204617)
98. Catalyst Molecular Simulations Inc, San Diego, CA, USA.
99. Dixon, S. L.; Smondyrev, A. M.; Knoll, E. H.; Rao, S. N.; Shaw, D. E.; Friesner, R. R. PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. J. Compu. Aided Mol. Des., 2006, 20, 647-671. (Pubitemid 46023928)
100. SYBYL/UNITY Tripos International, 1699 South Hanley Rd., St. Louis, Missouri, 63144, USA.
101. Schuffenhauer, A.; Gillet, V. J.; Willett, P. Similarity searching in files of three-dimensional chemical structures: analysis of the BIOSTER database using two-dimensional fingerprints and molecular field descriptors. J. Chem. Inf. Comput. Sci., 2000, 40, 295-307.
102. Cheeseright, T. J.; Mackey, M. D.; Melville, J. L.; Vinter, J. J. FieldScreen: virtual screening using molecular fields. Application to the DUD data set. J. Chem. Inf. Model., 2008, 48, 2108-2117.
103. Tervo, A. J.; Rönkkö, T.; Nyrönen, T. H.; Poso, A. BRUTUS: optimization of a grid-based similarity function for rigid-body molecular superposition. 1. Alignment and virtual screening applications. J. Med. Chem., 2005, 48, 4076-4086. (Pubitemid 40800616)
104. Kearsley, S. K.; Smith, G. G. An alternative method for the alignment of molecular structures: Maximizing electrostatic and steric overlap. Tetrahedron Comput. Meth., 1990, 3(6, Part 3), 615-633.
105. Marcou, G.; Rognan, D. Optimizing fragment and scaffold docking by use of molecular interaction fingerprints. J. Chem. inf. Model., 2007, 47, 195-207. (Pubitemid 46237537)
106. Ebalunode, J. O.; Ouyang, Z.; Liang, J.; Zheng, W. Novel approach to structure-based pharmacophore search using computational geometry and shape matching techniques. J. Chem. Inf. Model., 2008, 48, 889-901. (Pubitemid 351757754)
107. Brown, N.; Jacoby, E. On scaffolds and hopping in medicinal chemistry. Mini Rev. Med. Chem., 2006, 6(11), 1217-1229. (Pubitemid 44697101)
108. Proschak, E.; Rupp, M.; Derksen, S.; Schneider, G. Shapelets: Possibilities and limitations of shape-based virtual screening. J. Comput. Chem., 2008, 29, 108-114.
109. Vainio, M. J.; Puranen, J. S.; Johnson, M. M. ShaEP: molecular overlay based on shape and electrostatic potential. J. Chem. Inf. Model., 2009, 49, 492-502.
110. Sadowski, J.; Gasteiger, J.; Klebe, G. Comparison of automatic three-dimensional model builders using 639 X-ray structures. J. Chem. Inf. Comput. Sci., 1994, 34, 1000-1008.
111. Boström, J.; Greenwood, J. R.; Gottfries, J. Assessing the performance of OMEGA with respect to retrieving bioactive conformations. J. Mol. Graph. Model., 2003, 21, 449-462. (Pubitemid 36120775)
112. Grant, J. A.; Gallardo, M. A.; Pickup, B. B. A fast method of molecular shape comparison: a simple application of the Gaussian description of molecular shape. J. Comput. Chem., 1996, 17, 1653-1666. (Pubitemid 126535414)
113. 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, 1489-1495. (Pubitemid 40364556)
114. Mavridis, L.; Hudson, B. D.; Ritchie, D. D. Toward high throughput 3D virtual screening using spherical harmonic surface representations. J. Chem. Inf. Model., 2007, 47, 1787-1796. (Pubitemid 47561126)
115. Cai, W.; Shao, X.; Maigret, B. Protein-ligand recognition using spherical harmonic molecular surfaces: towards a fast and efficient filter for large virtual throughput screening. J. Mol. Graph. Model., 2002, 20, 313-328. (Pubitemid 34019229)
116. Ritchie, D. W.; Kemp, G. G. L. Protein docking using spherical polar Fourier correlations. Proteins, 2000, 39, 178-194. (Pubitemid 30176485)
117. Pérez-Nueno, V. I.; Ritchie, D. W.; Borrell, J. I.; Teixidó, J. Clustering and classifying diverse HIV entry inhibitors using a novel consensus shape-based virtual screening approach: further evidence for multiple binding sites within the CCR5 extracellular pocket. J. Chem. Inf. Model., 2008, 48, 2146-2165.
118. Pérez-Nueno, V. I.; Ritchie, D. W.; Rabal, O.; Pascual, R.; Borrell, J. I.; Teixidó, J. Comparison of ligand-based and receptor-based virtual screening of HIV entry inhibitors for the CXCR4 and CCR5 receptors using 3D ligand shape matching and ligand-receptor docking. J. Chem. Inf. Model., 2008, 48, 509-533. (Pubitemid 351535420)
119. Cai, W.; Xu, J.; Shao, X.; Leroux, V.; Beautrait, A.; Maigret, B. SHEF: a vHTS geometrical filter using coefficients of spherical harmonic molecular surfaces. J. Mol. Model., 2008, 14, 393-401.
120. Beautrait, A.; Leroux, V.; Chavent, M.; Ghemtio, L.; Devignes, M.-D.; Smaïl-Tabbone, M.; Cai, W.; Shao, X.; Moreau, G.; Bladon, P.; Yao, J.; Maigret, B. Multiple-step virtual screening using VSMG: overview and validation of fast geometrical matching enrichment. J. Mol. Model., 2008, 14, 135-148.
121. Kubinyi, H. 3D QSAR in drug design: theory, methods and applications, ESCOM: Leiden, Germany, 1993; p 759.
122. Gasteiger, J.; Li, X.; Uschold, A. The beauty of molecular surfaces as revealed by self-organizing neural networks. J. Mol. Graphics, 1994, 12(2), 90-97. (Pubitemid 24219248)
123. Cramer, R. D.; Patterson, D. E.; Bunce, J. J. Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J. Am. Chem. Soc., 1988, 110, 5959-5967.
124. 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. (Pubitemid 24379702)
125. Boström, J.; Böhm, M.; Gundertofte, K.; Klebe, G. A 3D QSAR study on a set of dopamine D4 receptor antagonists. J. Chem. Inf. Comput. Sci., 2003, 43, 1020-1027.
126. Assefa, H.; Kamath, S.; Buolamwini, J. J. 3D-QSAR and docking studies on 4-anilinoquinazoline and 4-anilinoquinoline epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors. J. Comput. Aided Mol. Des., 2003, 17, 475-493. (Pubitemid 38008961)
127. Caballero, J. 3D-QSAR (CoMFA and CoMSIA) and pharmacophore (GALAHAD) studies on the differential inhibition of aldose reductase by flavonoid compounds. J. Mol. Graph. Model., 2010, 29, 363-371.
128. Nagarajan, S.; Ahmed, A.; Choo, H.; Cho, Y. S.; Oh, K.-S.; Lee, B. H.; Shin, K. J.; Pae, A. A. 3D QSAR pharmacophore model based on diverse IKK. inhibitors. J. Mol. Model., 2011, 17, 209-218.
129. Spitzer, G. M.; Heiss, M.; Mangold, M.; Markt, P.; Kirchmair, J.; Wolber, G.; Liedl, K. K. One concept, three implementations of 3D pharmacophore-based virtual screening: distinct coverage of chemical search space. J. Chem. Inf. Model., 2010, 50, 1241-1247.
130. 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(17), 3233-3243.
131. An, J.; Totrov, M.; Abagyan, R. R. Pocketome via comprehensive identification and classification of ligand binding envelopes. Mol. Cell. Proteomics, 2005, 4, 752.
132. Campbell, S. J.; Gold, N. D.; Jackson, R. M.; Westhead, D. D. Ligand binding: functional site location, similarity and docking. Curr. Opin. Struct. Biol., 2003, 13, 389-395. (Pubitemid 36782467)
133. Gold, N. D.; Jackson, R. R. SitesBase: a database for structurebased protein-ligand binding site comparisons. Nucleic Acids Res., 2006, 34, D231-D234.
134. Blomberg, N.; Gabdoulline, R. R.; Nilges, M.; Wade, R. R. Classification of protein sequences by homology modeling and quantitative analysis of electrostatic similarity. Proteins, 1999, 37, 379-387. (Pubitemid 29519728)
135. Cruciani, G.; Goodford, P. P. A search for specificity in DNA-drug interactions. J. Mol. Graphics, 1994, 12(2), 116-129. (Pubitemid 24219251)
136. Baroni, M.; Cruciani, G.; Sciabola, S.; Perruccio, F.; Mason, J. J. A common reference framework for analyzing/comparing proteins and ligands. Fingerprints for Ligands and Proteins (FLAP): theory and application. J. Chem. Inf. Model., 2007, 47, 279-294. (Pubitemid 46615933)
137. Hetényi, C.; van der Spoel, D. Efficient docking of peptides to proteins without prior knowledge of the binding site. Protein Sci., 2002, 11, 1729-1737. (Pubitemid 34663553)
138. Bottegoni, G. Protein-ligand docking. Front. Biosci., 2011, 17, 2289-2306.
139. Ferrara, P.; Gohlke, H.; Price, D. J.; Klebe, G.; Brooks, C. C. I. Assessing scoring functions for protein-ligand interactions. J. Med. Chem., 2004, 47, 3032-3047. (Pubitemid 38702700)
140. 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.; Tedesco, G.; Wall, I. D.; Woolven, J. M.; Peishoff, C. E.; Head, M. M. A critical assessment of docking programs and scoring functions. J. Med. Chem., 2006, 49(20), 5912-5931. (Pubitemid 44484938)
141. Smith, R. D.; Dunbar, J. B.; Ung, P. M.-U.; Esposito, E. X.; Yang, C.-Y.; Wang, S.; Carlson, H. H. CSAR benchmark exercise of 2010: combined evaluation across all submitted scoring functions. J. Chem. Inf. Model., 2011, 51, 2115-2131.
142. Buzko, O. V.; Bishop, A. C.; Shokat, K. K. Modified AutoDock for accurate docking of protein kinase inhibitors. J. Comput. Aided Mol. Des., 2002, 16, 113-127. (Pubitemid 34855038)
143. Coupez, B.; Lewis, R. R. Docking and scoring - theoretically easy, practically impossible? Curr. Med. Chem., 2006, 13, 2995-3003. (Pubitemid 44509703)
144. Truchon, J.-F.; Bayly, C. C. Evaluating virtual screening methods: good and bad metrics for the "early recognition" problem. J. Med. Chem., 2007, 47, 488-508. (Pubitemid 46615951)
145. Cole, J. C.; Murray, C. W.; Nissink, J. J. M.; Taylor, R. D.; Taylor, R. Comparing protein-ligand docking programs is difficult. Proteins, 2005, 60, 325-332. (Pubitemid 41061629)
146. Enyedy, I. J.; Egan, W. W. Can we use docking and scoring for hittolead optimization ? J. Comput. Aided Mol. Des., 2008, 22, 161-168.
147. Friesner, R. A.; Murphy, R. B.; Repasky, M. P.; Frye, L. L.; Greenwood, J. R.; Halgren, T. A.; Sanschagrin, P. C.; Mainz, D. D. Extra precision Glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J. Med. Chem., 2006, 49, 6177-6196. (Pubitemid 44595196)
148. Rastelli, G.; Degliesposti, G.; Del Rio, A.; Sgobba, M. Binding estimation after refinement, a new automated procedure for the refinement and rescoring of docked ligands in virtual screening. Chem. Biol. Drug Des., 2009, 73, 283-286.
149. Thompson, D. C.; Humblet, C.; Joseph-McCarthy, D. Investigation of MM-PBSA rescoring of docking poses. J. Med. Chem., 2008, 48, 1081-1091.
150. Baker, N. N. Improving implicit solvent simulations: a Poissoncentric view. Curr. Opin. Struct. Biol., 2005, 15, 137-143.
151. Bouvier, G.; Evrard-Todeschi, N.; Girault, J.-P.; Bertho, G. Automatic clustering of docking poses in virtual screening process using self-organizing map. Bioinformatics, 2010, 26, 53-60.
152. Schellhammer, I.; Rarey, M. FlexX-Scan: fast, structure-based virtual screening. Proteins, 2004, 57(3), 504-517. (Pubitemid 39390805)
153. Patané, S.; Pietrancosta, N.; Hassani, H.; Leroux, V.; Maigret, B.; Kraus, J.-L.; Dono, R.; Maina, F. A new Met inhibitory-scaffold identified by a focused forward chemical biological screen. Biochem. Biophys. Res. Comm., 2008, 375, 184-189.
154. Teramoto, R.; Fukunishi, H. Supervised consensus scoring for docking and virtual screening. J. Chem. Inf. Model., 2007, 47, 526-534. (Pubitemid 46615954)
155. Erickson, J. A.; Jalaie, M.; Robertson, D. H.; Lewis, R. A.; Vieth, M. Lessons in molecular recognition: the effects of ligand and protein flexibility on molecular docking accuracy. J. Med. Chem., 2004, 47, 45-55. (Pubitemid 38040489)
156. Wolf, A.; Zimmermann, M.; Hofmann-Apitius, M. Alternative to consensus scoring-a new approach toward the qualitative combination of docking algorithms. J. Chem. Inf. Model., 2007, 47, 1036-1044. (Pubitemid 46973719)
157. Perola, E. Minimizing false positives in kinase virtual screens. Proteins, 2006, 64, 422-435. (Pubitemid 43993650)
158. Verdonk, M. L.; Cole, J. C.; Hartshorn, M. J.; Murray, C. W.; Taylor, R. R. Improved protein-ligand docking using GOLD. Proteins, 2003, 52, 609-623. (Pubitemid 37034158)
159. Jones, G.; Willett, P.; Glen, R. C.; Leach, A. R.; Taylor, R. Development and validation of a genetic algorithm for flexible docking. J. Mol. Biol., 1997, 267, 727-748. (Pubitemid 27170693)
160. Friesner, R. A.; Banks, J. L.; Murphy, R. B.; Halgren, T. A.; Klicic, J. J.; Mainz, D. T.; Repasky, M. P.; Knoll, E. H.; Shelley, M.; Perry, J. K.; Shaw, D. E.; Francis, P.; Shenkin, P. P. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem., 2004, 47, 1739-1749. (Pubitemid 38380917)
161. Halgren, T. A.; Murphy, R. B.; Friesner, R. A.; Beard, H. S.; Frye, L. L.; Pollard, W. T.; Banks, J. J. Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J. Med. Chem., 2004, 47, 1750-1759. (Pubitemid 38380918)
162. Kramer, B.; Rarey, M.; Lengauer, T. Evaluation of the FlexX incremental construction algorithm for protein-ligand docking. Proteins, 1999, 37, 228-241. (Pubitemid 29503004)
163. Rarey, M.; Kramer, B.; Lengauer, T.; Klebe, G. A fast flexible docking method using an incremental construction algorithm. J. Mol. Biol., 1996, 261, 470-489. (Pubitemid 26335901)
164. Morris, G. M.; Huey, R.; Lindstrom, W.; Sanner, M. F.; Belew, R. K.; Goodsell, D. S.; Olson, A. A. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J. Comput. Chem., 2009, 30, 2785-2791.
165. Huey, R.; Morris, G. M.; Olson, A. J.; Goodsell, D. D. A semiempirical free energy force field with charge-based desolvation. J. Comput. Chem., 2007, 28, 1145-1152. (Pubitemid 46506716)
166. Seeliger, D.; de Groot, B. B. Ligand docking and binding site analysis with PyMOL and Autodock/Vina. J. Comput. Aided Mol. Des., 2010, 24, 417-422.
167. Trott, O.; Olson, A. A. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem., 2010, 31, 455-461.
168. Moitessier, N.; Englebienne, P.; Lee, D.; Lawandi, J.; Corbeil, C. C. Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go. Br. J. Pharmacol., 2008, 153 Suppl, S7-S26. (Pubitemid 351340987)
169. Warren, G. L.; Andrews, C. W.; Capelli, A.-M.; Clarke, B.; LaLonde, J.;
170. Kellenberger, E.; Rodrigo, J.; Muller, P.; Rognan, D. Comparative evaluation of eight docking tools for docking and virtual screening accuracy. Proteins, 2004, 57, 225-242. (Pubitemid 39223729)
171. Cozzini, P.; Kellogg, G. E.; Spyrakis, F.; Abraham, D. J.; Costantino, G.; Emerson, A.; Fanelli, F.; Gohlke, H.; Kuhn, L. A.; Morris, G. M.; Orozco, M.; Pertinhez, T. A.; Rizzi, M.; Sotriffer, C. C. Target flexibility: an emerging consideration in drug discovery and design. J. Med. Chem., 2008, 51, 6237-6255.
172. Barril, X.; Fradera, X. Incorporating protein flexibility into docking and structure-based drug design. Exp. Opin. Drug Discov., 2006, 1, 335-349.
173. Teague, S. S. Implications of protein flexibility for drug discovery. Nat. Rev. Drug Discov., 2003, 2, 527-541.
174. Koshland, D. D. The key-lock theory and the induced fit theory. Angewandte Chemie Int. Ed. Eng., 1994, 33, 2375-2378.
175. DeWeese-Scott, C.; Moult, J. Molecular modeling of protein function regions. Proteins, 2004, 55, 942-961. (Pubitemid 38702952)
176. Oshiro, C.; Bradley, E. K.; Eksterowicz, J.; Evensen, E.; Lamb, M. L.; Lanctot, J. K.; Putta, S.; Stanton, R.; Grootenhuis, P. P. J. Performance of 3D-database molecular docking studies into homology models. J. Med. Chem., 2004, 47, 764-767. (Pubitemid 38129734)
177. Korb, O.; Stützle, T.; Exner, T. T. An ant colony optimization approach to flexible protein-ligand docking. SwarmIintel., 2007, 1, 115-134.
178. Henzler-Wildman, K.; Kern, D. Dynamic personalities of proteins. Nature, 2007, 450, 964-972. (Pubitemid 350273626)
179. Huang, S.-Y.; Zou, X. Ensemble docking of multiple protein structures: considering protein structural variations in molecular docking. Proteins, 2007, 66, 399-421. (Pubitemid 46053470)
180. Totrov, M.; Abagyan, R. R. Flexible ligand docking to multiple receptor conformations: a practical alternative. Curr. Opin. Struct. Biol., 2008, 18, 178-184.
181. Amaro, R. E.; Li, W. W. Emerging methods for ensemble-based virtual screening. Curr. Topics Med. Chem., 2010, 10(1), 3-13.
182. Asses, Y.; Leroux, V.; Tairi-Kellou, S.; Dono, R.; Maina, F.; Maigret, B. Analysis of c-Met kinase domain complexes: a new specific catalytic site receptor model for defining binding modes of ATP-competitive ligands. Chem. Biol. Drug Des., 2009, 74, 560-570.
183. Chipot, C.; Pohorille, A., Free energy calculations - theory and applications in chemistry and biology. Springer-Verlag: Berlin Heidelberg, 2007; p 517.
184. Michel, J.; Essex, J. J. Prediction of protein-ligand binding affinity by free energy simulations: assumptions, pitfalls and expectations. J. Comput Aided Mol. Des., 2010, 24, 639-658.
185. Christ, C. D.; Mark, A. E.; van Gunsteren, W. W. Basic ingredients of free energy calculations: a review. J. Comput. Chem., 2010, 31, 1469-1582.
186. Deng, Y.; Roux, B. Computations of standard binding free energies with molecular dynamics simulations. J. Phys. Chem. B, 2009, 113, 2234-2246.
187. Marlow, M. S.; Dogan, J.; Frederick, K. K.; Valentine, K. G.; Wand, A. A. The role of conformational entropy in molecular recognition by calmodulin. Nat. Chem. Biol., 2010, 6, 352-358.
188. Shoichet, B. B. No free energy lunch. Nat. Biotechnol., 2007, 25, 1109-1110.
189. Ladbury, J. E.; Klebe, G.; Freire, E. Adding calorimetric data to decision making in lead discovery: a hot tip. Nat. Rev. Drug Discov., 2010, 9, 23-27.
190. Ladbury, J. J. Isothermal titration calorimetry: application to structure-based drug design. Thermochimica Acta, 2001, 380, 209-215.
191. van Gunsteren, W. F.; Bakowies, D.; Baron, R.; Chandrasekhar, I.; Christen, M.; Daura, X.; Gee, P.; Geerke, D. P.; Glättli, A.; Hünenberger, P. H.; Kastenholz, M. A.; Oostenbrink, C.; Schenk, M.; Trzesniak, D.; van der Vegt, N. N. A.; Yu, H. H. Biomolecular modeling: Goals, problems, perspectives. Angew. Chem., 2006, 45, 4064-4092. (Pubitemid 44105659)
192. Karplus, M.; McCammon, J. J. Molecular dynamics simulations of biomolecules. Nat. Struct. Biol., 2002, 9, 646-652. (Pubitemid 34977295)
193. Case, D. A.; Cheatham, T. E., 3rd; Darden, T.; Gohlke, H.; Luo, R.; Merz, K. M., Jr.; Onufriev, A.; Simmerling, C.; Wang, B.; Woods, R. R. The Amber biomolecular simulation programs. J. Comput. Chem., 2005, 26(16), 1668-1688. (Pubitemid 43076180)
194. Brooks, B. R.; Brooks, C. C. I.; Mackerell, A. A. J.; Nilsson, L.; Petrella, R. J.; Roux, B.; Won, Y.; Archontis, G.; Bartels, C.; Boresch, S.; Caflisch, A.; Caves, L.; Cui, Q.; Dinner, A. R.; Feig, M.; Fischer, S.; Gao, J.; Hodoscek, M.; Im, W.; Kuczera, K.; Lazaridis, T.; Ma, J.; Ovchinnikov, V.; Paci, E.; Pastor, R. W.; Post, C. B.; Pu, J. Z.; Schaefer, M.; Tidor, B.; Venable, R. M.; Woodcock, H. L.; Wu, X.; Yang, W.; York, D. M.; Karplus, M. CHARMM: the biomolecular simulation program. J. Comput. Chem., 2009, 30, 1545-1614.
195. Christen, M.; Hünenberger, P. H.; Bakowies, D.; Baron, R.; Bürgi, R.; Geerke, D. P.; Heinz, T. N.; Kastenholz, M. A.; Kräutler, V.; Oostenbrink, C.; Peter, C.; Trzesniak, D.; van Gunsteren, W. W. The GROMOS software for biomolecular simulation: GROMOS05. J. Comput. Chem., 2005, 26, 1719-1751. (Pubitemid 43076183)
196. van der Spoel, D.; Lindahl, E.; Hess, B.; Groenhof, G.; Mark, A. E.; Berendsen, H. H. C. GROMACS: fast, flexible, and free. J. Comput. Chem., 2005, 26, 1701-1718. (Pubitemid 43076182)
197. Phillips, J. C.; Braun, R.; Wang, W.; Gumbart, J.; Tajkhorshid, E.; Villa, E.; Chipot, C.; Skeel, R. D.; Kalé, L.; Schulten, K. Scalable molecular dynamics with NAMD. J. Comput. Chem., 2005, 26, 1781-1802. (Pubitemid 43078511)
198. Kalé, L.; Skeel, R. D.; Bhandarkar, M.; Brunner, R.; Gursoy, A.; Krawetz, N.; Phillips, J. C.; Shinozaki, A.; Varadarajan, K.; Schulten, K. NAMD2: Greater scalability for parallel molecular dynamics. J. Comput. Phy., 1999, 151, 283-312.
199. Leroux, V.; Gresh, N.; Liu, W.-Q.; Garbay, C.; Maigret, B. Role of water molecules for binding inhibitors in the SH2 domain of Grb2: A molecular dynamics study. J. Mol. Struc. (THEOCHEM), 2007, 806, 51-66.
200. DiMaio, F.; Terwilliger, T. C.; Read, R. J.; Wlodawer, A.; Oberdorfer, G.; Wagner, U.; Valkov, E.; Alon, A.; Fass, D.; Axelrod, H. L.; Das, D.; Vorobiev, S. M.; Iwaï, H.; Pokkuluri, P. R.; Baker, D. Improved molecular replacement by density-and energy-guided protein structure optimization. Nature, 2011, 473, 540-543.
201. Trabuco, L. G.; Villa, E.; Schreiner, E.; Harrison, C. B.; Schulten, K. Molecular dynamics flexible fitting: a practical guide to combine cryo-electron microscopy and X-ray crystallography. Methods, 2009, 49, 174-180.
202. van Gunsteren, W. F.; Berendsen, H. H. C. Computer simulation of molecular dynamics: Methodology, applications, and perspectives in chemistry. Angew. Chem. Int. Ed., 1990, 29, 992-1023.
203. Yuriev, E.; Agostino, M.; Ramsland, P. P. Challenges and advances in computational docking: 2009 in review. J. Mol. Recognit., 2011, 24, 149-164.
204. Zhu, X.; Lopes, P. P. M.; Mackerell, A. A. J. Recent developments and applications of the CHARMM force fields. In: Wiley interdisciplinary reviews. Comput. Mol. Sci., 2012, 2, 167-185.
205. Dror, R. O.; Jensen, M. Ø.; Borhani, D. W.; Shaw, D. D. Exploring atomic resolution physiology on a femtosecond to millisecond timescale using molecular dynamics simulations. J. Gen. Physiol., 2010, 135, 555-562.
206. Shaw, D. E.; Maragakis, P.; Lindorff-Larsen, K.; Piana, S.; Dror, R. O.; Eastwood, M. P.; Bank, J. A.; Jumper, J. M.; Salmon, J. K.; Shan, Y.; Wriggers, W. Atomic-level characterization of the structural dynamics of proteins. Science, 2010, 330, 341-346.
207. Vanommeslaeghe, K.; Hatcher, E.; Acharya, C.; Kundu, S.; Zhong, S.; Shim, J.; Darian, E.; Guvench, O.; Lopes, P.; Vorobyov, I.; Mackerell Jr., A. A. CHARMM general force field: a force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J. Comput. Chem., 2010, 31, 671-690.
208. Guvench, O.; Greene, S. N.; Kamath, G.; Brady, J. W.; Venable, R. M.; Pastor, R. W.; Mackerell Jr, A. A. Additive empirical force field for hexopyranose monosaccharides. J. Comput. Chem., 2008, 29, 2543-2564.
209. Wang, J.; Wang, W.; Kollman, P. A.; Case, D. D. Automatic atom type and bond type perception in molecular mechanical calculations. J. Mol. Graphics Model., 2006, 25, 247-260. (Pubitemid 44363172)
210. Wang, J.; Wolf, R. M.; Caldwell, J. W.; Kollman, P. A.; Case, D. D. Development and testing of a general amber force field. J. Comput. Chem., 2004, 25, 1157-1174.
211. Malde, A. K.; Zuo, L.; Breeze, M.; Stroet, M.; Poger, D.; Nair, P. C.; Oostenbrink, C.; Mark, A. A. An Automated force field Topology Builder (ATB) and repository: version 1.0. J. Chem. Theory Comput., 2011, 7, 4026-4037.
212. Zoete, V.; Cuendet, M. A.; Grosdidier, A.; Michielin, O. SwissParam: a fast force field generation tool for small organic molecules. J. Comput. Chem., 2011, 32, 2359-2368.
213. Ball, P. Water as an active constituent in cell biology. Chem. Rev., 2008, 108, 74-108. (Pubitemid 351187472)
214. Homans, S. S. Water, water everywhere-except where it matters? Drug Discov. Today, 2007, 12(13-14), 534-539.
215. Paesani, F.; Voth, G. G. The properties of water: insights from quantum simulations. J. Phys. Chem. B, 2009, 113, 5702-5719.
216. Chandler, D. Interfaces and the driving force of hydrophobic assembly. Nature, 2005, 437, 640-647. (Pubitemid 41486526)
217. Ladbury, J. J. Just add water! The effect of water on the specificity of protein-ligand binding sites and its potential application to drug design. Chem. Biol., 1996, 3, 973-980.
218. Kirchmair, J.; Spitzer, G. M.; Liedl, K. K. Consideration of water and solvation effects in virtual screening. In Virtual screening: principles, challenges, and practical guidelines; C. A. Sotriffer, Ed. Wiley-VCH Verlag: 2011; pp 263-289.
219. Barillari, C.; Taylor, J.; Viner, R.; Essex, J. J. Classification of water molecules in protein binding sites. J. Am. Chem. Soc., 2007, 129, 2577-2587. (Pubitemid 46364065)
220. Zhang, L.; Hermans, J. Hydrophilicity of cavities in proteins. Proteins, 1996, 24, 433-438. (Pubitemid 26122057)
221. Rossato, G.; Ernst, B.; Vedani, A.; Smiesko, M. AcquaAlta: a directional approach to the solvation of ligand-protein complexes. J. Chem. Inf. Model., 2011, 51, 1867-1881.
222. Allen, F. F. The Cambridge Structural Database: a quarter of a million crystal structures and rising. Acta Crystallographica B, 2002, 58, 380-388.
223. Michel, J.; Tirado-Rives, J.; Jorgensen, W. W. Prediction of the water content in protein binding sites. J. Physical Chem. B, 2009, 113, 13337-13346.
224. Homans, S. S. Water, water everywhere - except where it matters? Drug Discov. Ttoday, 2007, 12, 534-539.
225. Beuming, T.; Che, Y.; Abel, R.; Kim, B.; Shanmugasundaram, V.; Sherman, W. Thermodynamic analysis of water molecules at the surface of proteins and applications to binding site prediction and characterization. Proteins, 2012, 80(3), 871-883.
226. Verdonk, M. L.; Chessari, G.; Cole, J. C.; Hartshorn, M. J.; Murray, C. W.; Nissink, J. J. M.; Taylor, R. D.; Taylor, R. Modeling water molecules in protein-ligand docking using GOLD. J. Med. Chem., 2005, 48, 6504-6515. (Pubitemid 41429002)
227. Huggins, D. J.; Tidor, B. Systematic placement of structural water molecules for improved scoring of protein-ligand interactions. Protein Eng. Des. Sel., 2011, 24, 777-789.
228. Huggins, D. J.; McKenzie, G. J.; Robinson, D. D.; Narváez, A. J.; Hardwick, B.; Roberts-Thomson, M.; Venkitaraman, A. R.; Grant, G. H.; Payne, M. M. Computational analysis of phosphopeptide binding to the polo-box domain of the mitotic kinase PLK1 using molecular dynamics simulation. PLoS Comput. Biol., 2010, 6, e1000880.
229. Verdonk, M. L.; Cole, J. C.; Taylor, R. SuperStar: A knowledgebased approach for identifying interaction sites in proteins. J. Mol. Biol., 1999, 289(4), 1093-1108. (Pubitemid 29306672)
230. Raymer, M. L.; Sanschagrin, P. C.; Punch, W. F.; Venkataraman, S.; Goodman, E. D.; Kuhn, L. L. Predicting conserved watermediated and polar ligand interactions in proteins using a Knearestneighbors genetic algorithm. J. Mol. Biol., 1997, 265(4), 445-464. (Pubitemid 27110680)
231. Amadasi, A.; Surface, J. A.; Spyrakis, F.; Cozzini, P.; Mozzarelli, A.; Kellogg, G. G. Robust classification of "relevant" water molecules in putative protein binding sites. J. Med. Chem., 2008, 51(4), 1063-1067. (Pubitemid 351304716)
232. Garcia-Sosa, A. T.; Mancera, R. L.; Dean, P. P. WaterScore: a novel method for distinguishing between bound and displaceable water molecules in the crystal structure of the binding site of protein-ligand complexes. J. Mol. Model., 2003, 9(3), 172-182. (Pubitemid 37240075)
233. Kortvelyesi, T.; Dennis, S.; Silberstein, M.; Brown, L., 3rd; Vajda, S. Algorithms for computational solvent mapping of proteins. Proteins, 2003, 51(3), 340-351. (Pubitemid 36528820)
234. Fradera, X.; Mestres, J. Guided docking approaches to structurebased design and screening. Curr. Top. Med. Chem., 2004, 4(7), 687-700. (Pubitemid 38842585)
235. Wolber, G.; Langer, T. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J. Chem. Inf. Model., 2005, 45(1), 160-169. (Pubitemid 40736968)
236. Wolber, G.; Seidel, T.; Bendix, F.; Langer, T. Moleculepharmacophore superpositioning and pattern matching in computational drug design. Drug Discov. Today, 2008, 13, 23-29.
237. Chen, J.; Lai, L. L. Pocket v. 2: Further developments on receptorbased pharmacophore modeling. J. Chem. Inf. Model., 2006, 46(6), 2684-2691. (Pubitemid 46008137)
238. Ortuso, F.; Langer, T.; Alcaro, S. GBPM: GRID-based pharmacophore model: concept and application studies to protein-protein recognition. Bioinformatics, 2006, 22(12), 1449-1455. (Pubitemid 43985315)
239. Hindle, S. A.; Rarey, M.; Buning, C.; Lengauer, T. Flexible docking under pharmacophore type constraints. J. Comput. Aided Mol. Des., 2002, 16, 129-149. (Pubitemid 34855039)
240. Wong, R. R. Y.; Bodart, V.; Metz, M.; Labrecque, J.; Bridger, G.; Fricker, S. S. Comparison of the potential multiple binding modes of Bicyclam, Monocylam, and Noncyclam small-molecule CXC chemokine receptor 4 inhibitors. Mol. Pharmacol., 2008, 74, 1485-1495.
241. Kellenberger, E.; Springael, J.-Y.; Parmentier, M.; Hachet-Haas, M.; Galzi, J.-L.; Rognan, D. Identification of nonpeptide CCR5 receptor agonists by structure-based virtual screening. J. Med. Chem., 2007, 50, 1294-1303. (Pubitemid 46504976)
242. Brewerton, S. The use of protein-ligand interaction fingerprints in docking. Curr. Opin. Drug Discov. Develop., 2008, 11(3), 356-364. (Pubitemid 351572445)
243. Deng, Z.; Chuaqui, C.; Singh, J. Structural interaction fingerprint (SIFt): a novel method for analyzing three-dimensional proteinligand binding interactions. J. Med. Chem., 2004, 47, 337-344. (Pubitemid 38057878)
244. Weill, N.; Rognan, D. Alignment-free ultra-high-throughput comparison of druggable protein-ligand binding sites. J. Chem. Inf. Model., 2010, 50, 123-135.
245. Wassermann, A. M.; Geppert, H.; Bajorath, J. Searching for targetselective compounds using different combinations of multiclass support vector machine ranking methods, kernel functions, and fingerprint descriptors. J. Chem. Inf. Model., 2009, 49, 582-592.
246. Kolb, H. C.; Sharpless, K. K. The growing impact of click chemistry on drug discovery. Drug Discov. Today, 2003, 8, 1128-1137. (Pubitemid 37547919)
247. Lewis, W. G.; Green, L. G.; Grynszpan, F.; Radi, Z.; Carlier, P. R.; Taylor, P.; Finn, M. G.; Sharpless, K. K. Click chemistry in situ: acetylcholinesterase as a reaction vessel for the selective assembly of a femtomolar inhibitor from an array of building blocks. Angewandte Chemie, 2002, 41, 1053-1057. (Pubitemid 34251912)
248. Ekins, S.; Mestres, J.; Testa, B. In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Brit. J. Pharmacol., 2007, 152, 9-20. (Pubitemid 47339896)
249. Bajorath, J. Computational analysis of ligand relationships within target families. Curr. Opin. Chem. Biol., 2008, 12, 352-358.
250. 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. K. H.; Edwards, D. D.; Shoichet, B. K.; Roth, B. B. Predicting new molecular targets for known drugs. Nature, 2009, 462, 175-181.
251. Qu, X. A.; Gudivada, R. C.; Jegga, A. G.; Neumann, E. K.; Aronow, B. B. Inferring novel disease indications for known drugs by semantically linking drug action and disease mechanism relationships. BMC bioinformatics, 2009, 10 Suppl 5, S4.
252. Paul, S. M.; Mytelka, D. S.; Dunwiddie, C. T.; Persinger, C. C.; Munos, B. H.; Lindborg, S. R.; Schacht, A. A. How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nat. Rev. Drug Discov., 2010, 9, 203-214.
253. van de Waterbeemd, H.; Gifford, E. ADMET in silico modelling: towards prediction paradise? Nat. Rev. Drug Discov., 2003, 2, 192-204. (Pubitemid 37361664)
254. Krämer, S. S. Absorption prediction from physicochemical parameters. Pharm. Sci. Technolo. Today, 1999, 2, 373-380.
255. Chohan, K. K.; Paine, S. W.; Waters, N. N. Advancements in predictive in silico models for ADME. Curr. Chem. Biol., 2008, 2, 215-228.
256. Kirchmair, J.; Distinto, S.; Schuster, D.; Spitzer, G.; Langer, T.; Wolber, G. Enhancing drug discovery through in silico screening: strategies to increase true positives retrieval rates. Curr. Med. Chem., 2008, 15, 2040-2053.
257. Hristovski, D.; Deroski, S.; Peterlin, B.; Roi-Hristovski, A. Supporting discovery in medicine by association rule mining of bibliographic databases. Stud. Health Technol. Inform., 2001, 84, 1344-1348.
258. Abdulla, G.; Awwal, A.; Borne, K.; Ho, T. K.; Vestrand, W. W. Practical data mining and machine learning for optics applications: introduction to the feature issue. App. Opt., 2011, 50, PDM1-2.
259. Ghose, A. K.; Herbertz, T.; Hudkins, R. L.; Dorsey, B. D.; Mallamo, J. J. Knowledge-based, central nervous system (CNS) lead selection and lead optimization for CNS drug discovery. ACS Chem. neurosci., 2012, 3(1), 50-68.
260. Ghose, A. K.; Herbertz, T.; Pippin, D. A.; Salvino, J. M.; Mallamo, J. J. Knowledge based prediction of ligand binding modes and rational inhibitor design for kinase drug discovery. J. Med. Chem., 2008, 51(17), 5149-5171.
261. Simmons, K.; Kinney, J.; Owens, A.; Kleier, D. A.; Bloch, K.; Argentar, D.; Walsh, A.; Vaidyanathan, G. Practical outcomes of applying ensemble machine learning classifiers to High-Throughput Screening (HTS) data analysis and screening. J. Chem. Inf. Model., 2008, 48, 2196-2206.
262. Blundell, T. L.; Sibanda, B. L.; Sternberg, M. J.; Thornton, J. J. Knowledge-based prediction of protein structures and the design of novel molecules. Nature, 1987, 326(6111), 347-352. (Pubitemid 17053762)
263. Deng, Z.; Chuaqui, C.; Singh, J. Knowledge-based design of targetfocused libraries using protein-ligand interaction constraints. J. Med. Chem., 2006, 49(2), 490-500. (Pubitemid 43157483)
264. Li, L.; Wang, B.; Meroueh, S. S. Support vector regression scoring of receptor-ligand complexes for rank-ordering and virtual screening of chemical libraries. J. Chem. Inf. Model., 2011, 51, 2132-2138.
265. Xue, M. Z.; Zheng, M. Y.; Xiong, B.; Li, Y. L.; Jiang, H. L.; Shen, J. J. Knowledge-Based Scoring Functions in Drug Design. 1. Developing a Target-Specific Method for Kinase-Ligand Interactions. J. Chem. Inf. Model., 2010, 50(8), 1378-1386.
266. Ghemtio, L.; Devignes, M.-D.; Smaïl-Tabbone, M.; Souchet, M.; Leroux, V.; Maigret, B. Comparison of three preprocessing filters efficiency in virtual screening: identification of new putative LXRß regulators as a test case. J. Chem. Inf. Model., 2010, 50, 701-715.
267. Seoud, R. R. BioHCVKD: a bioinformatics knowledge discovery system for HCV drug discovery - identifying proteins, ligands and active residues, in biological literature. Int. J. Bioinform. Res. App., 2011, 7(3), 317-333.
268. Medina-Aunon, J. A.; Paradela, A.; Macht, M.; Thiele, H.; Corthals, G.; Albar, J. J. Protein information and knowledge extractor: Discovering biological information from proteomics data. Proteomics, 2010, 10, 3262-3271.
269. Zheng, M. Y.; Xiong, B.; Luo, C.; Li, S. S.; Liu, X.; Shen, Q. C.; Li, J.; Zhu, W. L.; Luo, X. M.; Jiang, H. H. Knowledge-based scoring functions in drug design: 3. A two-dimensional knowledgebased hydrogen-bonding potential for the prediction of proteinligand interactions. J. Chem. Inf. Model., 2011, 51(11), 2994-3004.
270. Shen, Q. C.; Xiong, B.; Zheng, M. Y.; Luo, X. M.; Luo, C.; Liu, X. A.; Du, Y.; Li, J.; Zhu, W. L.; Shen, J. K.; Jiang, H. H. Knowledgebased scoring functions in drug design: 2. Can the knowledge base be enriched? J. Chem. Inf. Model., 2011, 51(2), 386-397.
271. Ghose, A. K.; Herbertz, T.; Salvino, J. M.; Mallamo, J. J. Knowledge-based chemoinformatic approaches to drug discovery. Drug Discov. Today, 2006, 11(23-24), 1107-1114. (Pubitemid 44803112)
272. Pérez-Nueno, V. I.; Pettersson, S.; Ritchie, D. W.; Borrell, J. I.; Teixidó, J. Discovery of novel HIV entry inhibitors for the CXCR4 receptor by prospective virtual screening. J. Chem. Inf. Model., 2009, 49, 810-823.
273. Wang, J.; Kollman, P. A.; Kuntz, I. I. Flexible ligand docking: a multistep strategy approach. Proteins, 1999, 36, 1-19. (Pubitemid 29260413)
274. Sauton, N.; Lagorce, D.; Villoutreix, B. O.; Miteva, M. M. MSDOCK: accurate multiple conformation generator and rigid docking protocol for multi-step virtual ligand screening. BMC bioinformatics, 2008, 9, 184.
275. Beaulah, S. A.; Correll, M. A.; Munro, R. E.; Sheldon, J. J. Addressing informatics challenges in Translational Research with workflow technology. Drug Discov. Today, 2008, 13(17-18), 771-777.
276. Tiwari, A.; Sekhar, A. A. Workflow based framework for life science informatics. Comput. Biol. Chem., 2007, 31(5-6), 305-319. (Pubitemid 47531992)
277. Berthold, M. R.; Cebron, N.; Dill, F.; Gabriel, T. R.; Kötter, T.; Meinl, T.; Ohl, P.; Thiel, K.; Wiswedel, B. KNIME - The Konstanz Information Miner. SIGKDD Explorations, 2008, 11, 26-31.
278. Pipeline Pilot Accelrys Inc., San Diego, CA, USA.
279. Inforsense ID Business Solutions Ltd., Guildford, Surrey, UK.
280. Hassan, M.; Brown, R. D.; Varma-O'brien, S.; Rogers, D. Cheminformatics analysis and learning in a data pipelining environment. Mol. Divers, 2006, 10(3), 283-99. (Pubitemid 44546683)
281. Collins, J. J. Therapeutic opportunities for liver X receptor modulators. Curr. Opin. Drug Discov. Dev., 2004, 7(5), 692-702.
282. Ghemtio, L.; Smail-Tabbone, M.; Devignes, M.-D.; Souchet, M.; Maigret, B. In: A KDD approach for designing filtering strategies to improve virtual screening, Procedings of the KDIR - International Conference on Knowledge Discovery and Information Retrieval, Madeira, Portugal, 2009-10-06, 2009-10-09; F. Ana, Ed; INSTIC: Madeira, Portugal, 2009.
283. Palczewski, K.; Kumasaka, T.; Hori, T.; Behnke, C. A.; Motoshima, H.; Fox, B. A.; Le Trong, I.; Teller, D. C.; Okada, T.; Stenkamp, R. E.; Yamamoto, M.; Miyano, M. Crystal structure of Rhodopsin: a G protein-coupled receptor. Science, 2000, 289, 739-745. (Pubitemid 30650186)
284. Carrieri, A.; Pérez-Nueno, V. I.; Fano, A.; Pistone, C.; Ritchie, D. W.; Teixidó, J. Biological profiling of anti-HIV agents and insight into CCR5 antagonist binding using in silico techniques. ChemMedChem., 2009, 4, 1153-1163.
285. Pettersson, S.; Pérez-Nueno, V. I.; Ros-Blanco, L.; Puig de La Bellacasa, R.; Rabal, M. O.; Batllori, X.; Clotet, B.; Clotet-Codina, I.; Armand-Ugón, M.; Esté, J.; Borrell, J. I.; Teixidó, J. Discovery of novel non-cyclam polynitrogenated CXCR4 coreceptor inhibitors. ChemMedChem., 2008, 3, 1549-1557.
286. Liu, J.; Zhang, H.; Xiao, Z.; Wang, F.; Wang, X.; Wang, Y. Combined 3D-QSAR, molecular docking and molecular dynamics study on derivatives of peptide epoxyketone and tyropeptin-boronic acid as inhibitors against the..5 subunit of human 20S proteasome. Int. J. Mol. Sci., 2011, 12, 1807-1835.
287. Shih, K.-C.; Lin, C.-Y.; Zhou, J.; Chi, H.-C.; Chen, T.-S.; Wang, C.-C.; Tseng, H.-W.; Tang, C.-Y. Development of novel 3D-QSAR combination approach for screening and optimizing B-Raf inhibitors in silico. J. Chem. Inf. Model., 2011, 51, 398-407.
288. Kubinyi, H. Success stories of computer-aided design. In Computer applications in pharmaceutical research and development; S. Ekins, Ed. Wiley-Interscience: New York, 2006; pp 377-424.
289. Kirchmair, J.; Distinto, S.; Markt, P.; Schuster, D.; Spitzer, G. M.; Liedl, K. R.; Wolber, G. How to optimize shape-based virtual screening: choosing the right query and including chemical information. J. Chem. Inf. Model., 2009, 49, 678-692.