Extraction and visualization of potential pharmacophore points using support vector machines: Application to ligand-based virtual screening for COX-2 inhibitors

Journal of Medicinal Chemistry

Volumn 48, Issue 22, 2005, Pages 6997-7004

  Franke, Lutz ^a   Byvatov, Evgeny ^a   Werz, Oliver ^a   Steinhilber, Dieter ^a   Schneider, Petra ^b   Schneider, Gisbert ^a  

^a GOETHE UNIVERSITY   (Germany)

^b Schneider Consulting GbR   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

4 [5 (4 BROMOPHENYL) 3 TRIFLUOROMETHYL 1H PYRAZOL 1 YL]BENZENESULFONAMIDE; ARGATROBAN; BENZIMIDAZOLE DERIVATIVE; CELECOXIB; CYCLOOXYGENASE 2; CYCLOOXYGENASE 2 INHIBITOR; DICLOFENAC; NALPHA (2 NAPHTHALENESULFONYLGLYCYL) 4 AMIDINO PHENYLALANINEPIPERIDINE; ROFECOXIB; THROMBIN; THROMBIN INHIBITOR; UNCLASSIFIED DRUG;

ARTICLE; ASSAY; CLASSIFICATION; COMPUTER PROGRAM; CONTROLLED STUDY; DATA BASE; DRUG DESIGN; DRUG SCREENING; ENZYME INHIBITION; EXTRACTION; HUMAN; HUMAN CELL; IC 50; LEARNING; LIGAND BINDING; PHARMACOPHORE; SUPPORT VECTOR MACHINE; THREE DIMENSIONAL IMAGING; VALIDATION PROCESS; VIRTUAL REALITY;

BINDING SITES; CELL LINE; CYCLOOXYGENASE 2; CYCLOOXYGENASE 2 INHIBITORS; ENZYME-LINKED IMMUNOSORBENT ASSAY; HUMANS; LIGANDS; MODELS, MOLECULAR; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; THROMBIN;

EID: 27444433232     PISSN: 00222623     EISSN: None     Source Type: Journal    
DOI: 10.1021/jm050619h     Document Type: Article

References (62)

1. Dror, O.; Shulman-Peleg, A.; Nussinov, R.; Wolfson, H. J. Predicting molecular interactions in silico: I. A guide to pharmacophore identification and its applications to drug design. Curr. Med. Chem. 2004, 11, 71-90.
2. S. Pickett, The biophore concept. In Protein-Ligand Interactions; Böhm, H.-J., Schneider, G., Eds.); Wiley-VCH: Weinheim, 2003; pp 73-105.
3. Guner, O. F. History and evolution of the pharmacophore concept in computer-aided drug design. Curr. Top. Med. Chem. 2002, 2, 1321-1332.
4. Kurogy, Y.; Guner, O. F. Pharmacophore modeling and three-dimensional database searching for drug design using Catalyst. Curr. Med. Chem. 2001, 8, 1035-1055.
5. Martin, Y. C.; Bures, M. G.; Danaher, E. A.; DeLazzer, J.; Lico, I.; Pavlik, P. A. A fast approach to pharmacophore mapping and its application to dopaminergic and benzodiazepine agonists. J. Comput.-Aided Mol. Des. 1993, 7, 83-102.
6. 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, 160-169.
7. Cruciani, G.; Watson, K. A. 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, 7, 2589-2601.
8. Sippl, W. Receptor-based 3D QSAR analysis of estrogen receptor ligands - merging the accuracy of receptor-based alignments with the computational efficiency of ligand-based methods. J. Comput.-Aided Mol. Des. 2000, 14, 559-572.
9. Horvath, D.; Jeandenans, C. Neighborhood behavior of in silico structural spaces with respect to in vitro activity spaces-a novel understanding of the molecular similarity principle in the context of multiple receptor binding profiles. J. Chem. Inf. Comput. Sci. 2003, 43, 680-690.
10. Renner, S.; Schneider, G. Fuzzy pharmacophore models from molecular alignments for correlation-vector based virtual screening. J. Med. Chem. 2004, 47, 4653-4664.
11. Sheridan, R. P.; Miller, M. D.; Underwood, D. J.; Kearsley, S. K. Chemical similarity using geometric atom pair descriptors. J. Chem. Inf. Comput. Sci. 1996, 36, 128-136.
12. McGregor, M. J.; Muskal, S. M. Pharmacophore fingerprinting. 1. Application to QSAR and focused library design. J. Chem. Inf. Comput. Sci. 1999, 39, 569-574.
13. Vapnik, V., The Nature of Statistical Learning Theory. In Ed. 1995: Springer
14. Byvatov, E.; Schneider, G. Applications of support vector machines in bioinformatics. Appl. Bioinf. 2003, 2, 67-77.
15. Byvatov, E.; Sasse, B. C.; Stark, H.; Schneider, G. From virtual to real screening for novel D3 dopamine receptor ligands. ChemBioChem 2005, 6, 997-999.
16. Müller, K. R.; Ratsch, G.; Sonnenburg, S.; Mika, S.; Grimm, M.; Heinrich, N. Classifying 'drug-likeness' with Kernel-based learning methods. J. Chem. Inf. Model. 2005, 45, 249-253.
17. Zernov, V. V.; Balakin, K. V.; Ivaschenko, A. A.; Savchuk, N. P.; Pletnev, I. V. Drug discovery using support vector machines. The case studies of drug-likeness, agrochemical-likeness, and enzyme inhibition predictions. J. Chem. Inf. Comput. Sci. 2003, 43, 2048-2056.
18. Byvatov, E.; Schneider, G. SVM-based feature selection for characterization of focused compound collections, J. Chem. Inf. Comput. Sci., 2004, 44, 993-999.
19. Byvatov, E.; Fechner, U.; Sadowski, J.; Schneider, G. Comparison of support vector machine and artificial neural network systems for drug/nondrug classification. J. Chem. Inf. Comput. Sci. 2003, 43, 1882-1889.
20. Warmuth, M. K.; Liao, J.; Ratsch, G.; Mathieson, M.; Putta, S.; Lemmen, C. Active learning with support vector machines in the drug discovery process. J. Chem. Inf. Comput. Sci. 2003, 43, 667-673.
21. Schneider, P.; Schneider, G. Collection of bioactive reference compounds for focused library design. QSAR Comb. Sci. 2003, 22, 713-718.
22. Irwin, J. J.; Shoichet, B. K. ZINC- -a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model. 2005, 45, 177-82.
23. Sheridan, R. P.; Bush, B. L. PATTY: A programmable atom typer and language for automatic classification of atoms in molecular databases. J. Chem. Inf. Comput. Sci. 1993, 33, 756-762.
24. Cristianini, N.; Shawe-Taylor, J. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press: Cambridge, 2000.
25. Burges, C. J. C. A tutorial on support vector machines for pattern recognition. Data Mining Knowledge Discovery 1998, 2, 121-167.
26. Cortes, C.; Vapnik, V. Support-vector networks. Machine Learning 1995, 20, 273-297.
27. Joachims, T. Making large-Scale SVM learning practical, in: Advances in Kernel Methods - Support Vector Learning (Schölkopf, B., Burges, C., Smola, A., Eds.), MIT-Press: Cambridge, MA, 1999; pp 41-56.
28. Duda, R. O.; Hart, P. E.; Stork, D. G. Pattern Classification; Wiley-Interscience: New York, 2000.
29. Tong, S.; Koller, D. Support vector machine active learning with applications to text classification. In Proceedings of the Seventeenth International Conference on Machine Learning; Morgan Kaufmann: San Francisco, CA, 2000.
30. Halgren, T. A., The Merck force field, J. Comput. Chem. 1996, 17, 490-512.
31. Baxter, C. A.; Murray, C. W.; Clark, D. E.; Westhead, D. R.; Eldridge, M. D., Flexible docking using tabu search and an empirical estimate of binding affinity. Proteins Struct. Funct. Genet. 1998, 33, 367-382.
32. Schneider, P.; Schneider, G. Navigation through chemical space: ligand-based library design of focused compound libraries. In Chemogenomics in Drug Discovery; Kubinyi, H., Müller, G., Eds.); Wiley-VCH: Weinheim, 2004; pp 341-376.
33. Palomer, A.; Cabre, F.; Pascual, J.; Campos, J.; Trujillo, M. A.; Entrena, A.; Gallo, M. A.; Garcia, L.; Mauleon, D.; Espinosa, A. Identification of novel cyclooxygenase-2 selective inhibitors using pharmacophore models. J. Med. Chem. 2002, 45, 1402-1411.
34. Kalgutkar, A. S.; Zhao, Z. Discovery and design of selective cyclooxygenase-2 inhibitors as nonulcerogenic, antiinflammatory drugs with potential utility as anti-cancer agents. Curr. Drug Targets 2001, 2, 79-106.
35. Kozak, K. R.; Prusakiewicz, J. J.; Rowlinson, S. W.; Schneider, C.; Marnett, L. J. Amino acid determinants in cyclooxygenase-2 oxygenation of the endocannabinoid 2-arachidonylglycerol. J. Biol. Chem. 2001, 276, 30072-30077.
36. Merck Frosst Canada, Inc. US5849943, 1998.
37. Merck Frosst Canada, Inc. US5789413, 1998.
38. Merck Frosst Canada, Inc. US5733909, 1998.
39. Lab, UPSA, PCT WO9815528, 1998.
40. Banner, D. W. Principles of enzyme-inhibitor design. In: Protein-Ligand Interactions; Böhm, H.-J., Schneider, G. Eds., Wiley-VCH: Weinheim, 2003; pp 163-185.
41. Hilpert, K.; Ackermann, J.; Banner, D. W.; Gast, A.; Gubernator, K.; Hadvary, P.; Labler, L.; Müller, K.; Schmid, G.; Tschopp, T. B.; van de Waterbeemd, H. Design and synthesis of potent and highly selective thrombin inhibitors. J. Med. Chem. 1994, 37, 3889-3901.
42. Patel, Y.; Gillet, V. J.; Bravi, G.; Leach, A. R. A comparison of the pharmacophore identification programs: Catalyst, DISCO and GASP. J. Comput.-Aided Mol. Des. 2002, 16, 653-681.
43. Berliner, L. J. Thrombin: Structure and Function; Plenum Press: New York, 1992.
44. Kimball, S. D. Challenges in the development of orally bioavailable thrombin active site inhibitors. Blood Coagulation Fibrinolysis 1995, 6, 511-519.
45. Kikumoto, R.; Tamao, Y.; Tezuka, T.; Tonomura, S.; Hara, H.; Ninomiya, K.; Hijikata, A.; Okamoto, S. Selective inhibition of thrombin by (2R,4R)-4-methyl-1-[N2-[(3-methyl-1,2,3,4-tetrahydro-8-quinolinyl)sulfonyl] -1-arginyl)]-2-piperidinecarboxylic acid. Biochemistry 1984, 23, 85-90.
46. Sturzebecher, J.; Markwardt, F.; Viogt, B.; Wagner, G.; Walsmann, P. Cyclic amides of N alpha-arylsulfonylaminoacylated 4-amidinophenylalanine-tight binding inhibitors of thrombin. Thromb. Res. 1983, 29, 635-642.
47. Kato, M.; Nishida, S.; Kitasato, H.; Sakata, N.; Kawai, S. Cyclooxygenase-1 and cyclooxygenase-2 selectivity of nonsteroidal antiinflammatory drugs: investigation using human peripheral monocytes. J. Pharm. Pharmacol. 2001, 53, 1679-1685.
48. Penning, T. D.; et al. Synthesis and biological evaluation of the 1,5-diarylpyrazole class of cyclooxygenase-2 inhibitors: identification of 4-[5-(4-methylphenyl)-3-(trifluoromethyl)-1H-pyrazol-1-yl]benzenesulfonamide (SC-58635, celecoxib). J. Med. Chem. 1997, 40, 1347-1365.
49. Kurumbail, R. G.; Stevens, A. M.; Gierse, J. K.; McDonald, J. J.; Stegeman, R. A.; Pak, J. Y.; Gildehaus, D.; Miyashiro, J. M.; Penning, T. D.; Seibert, K.; Isakson, P. C.; Stallings, W. C. Structural basis for selective inhibition of cyclooxygenase-2 by anti-inflammatory agents. Nature 1996, 384, 644-648.
50. Ulbrich, H.; Fiebich, B.; Dannhardt, G. Cyclooxygenase-1/2 (COX-1/COX-2) and 5-lipoxygenase (5-LOX) inhibitors of the 6,7-diaryl-2, 3-1H- dihydropyrrolizine type. Eur. J. Med. Chem. 2002, 37, 953-959.
51. Laufer, S.; Neher, K.; Striegel, H. G. Anti-inflammatory oxo and hydroxy derivatives of pyrrolizines and their pharmaceutical use. WO-00105792, 2001.
52. Albert, D.; Zündorf, I.; Dingermann, T.; Müller, W. E.; Steinhilber, D.; Werz, O. Hyperforin is a dual inhibitor of cyclooxygenase-1 and 5-lipoxygenase. Biochem. Pharmacol. 2002, 64, 1767-1775.
53. Charlier, C.; Michaux, C. Dual inhibition of cyclooxygenase-2 (COX-2) and 5-lipoxygenase (5-LOX) as a new strategy to provide safer nonsteroidal anti-inflammatory drugs. Eur. J. Med. Chem. 2003, 38, 645-659.
54. Flower, R. J.; Vane, J. R. Inhibition of prostaglandin synthetase in brain explains the anti-pyretic activity of paracetamol (4-acetamidophenol). Nature 1972, 240, 410-411.
55. Smith, W. L.; Garavito, R. M.; Dewitt, D. L. Prostaglandin endoperoxide H synthases (cyclooxygenases)-1 and -2. J. Biol. Chem. 1996, 271, 33157-33160.
56. Trummlitz, G.; van Ryn, J. Designing selective COX-2 inhibitors: molecular modeling approaches. Curr. Opin. Drug Discovery Dev. 2002, 5, 550-561.
57. Burbidge R, Trotter M, Buxton B, Holden S. Drug design by machine learning: support vector machines for pharmaceutical data analysis. Comput. Chem. 2001, 26, 5-14.
58. Liu, Y. A comparative study on feature selection methods for drug discovery. J. Chem. Inf. Comput. Sci. 2004, 44, 1823-1828.
59. Jones, G.; Willett, P.; Glen, R. C. A genetic algorithm for flexible molecular overlay and pharmacophore elucidation. J. Comput.-Aided Mol. Des. 1995, 9, 532-549.
60. Brungs, M.; Rådmark, O.; Samuelsson, B.; Steinhilber, D. Sequential induction of 5-lipoxygenase gene expression and activity in Mono Mac 6 cells by transforming growth factor-beta and 1,25-dihydroxyvitamin D3. Proc. Natl. Acad. Sci. U.S.A. 1995, 92, 107-111.
61. Brune, K.; Reinke, M.; Lanz, R.; Peskar, B. A. Monoclonal antibodies against E- and F-type prostaglandins. High specificity and sensitivity in conventional radioimmunoassays. FEBS Lett. 1985, 186, 46-50.
62. Yamamoto, S.; Yokota, K.; Tonai, T.; Shono, F.; Hayashi, Y. Enzyme Immunoassay. Prostaglandins and Related Substances - A Practical Approach. IRL Press: Oxford, 1987,