3D pharmacophores as tools for activity profiling

Drug Discovery Today: Technologies

Volumn 7, Issue 4, 2010, Pages

  Schuster, Daniela ^a  

^a UNIVERSITY OF INNSBRUCK   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

NATURAL PRODUCT;

ARTICLE; COMPUTER MODEL; COMPUTER PROGRAM; DRUG DEVELOPMENT; DRUG REPOSITIONING; ETHNOPHARMACOLOGY; EVALUATION RESEARCH; PHARMACOPHORE;

EID: 78650750102     PISSN: 17406749     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ddtec.2010.11.006     Document Type: Review

References (40)

1. A.R. Leach Three-dimensional pharmacophore methods in drug discovery J. Med. Chem. 53 2010 539 558
2. P. Markt Pharmacophore models for virtual screening C. Sotriffer, Virtual Screening 2011 Wiley-VCH 115 152
3. D. Schuster Predicting cyclooxygenase inhibition by three-dimensional pharmacophoric profiling. Part I: Model generation, validation and applicability in ethnopharmacology Mol. Inf. 29 2010 75 86
4. H. Berman Announcing the worldwide Protein Data Bank Nat. Struct. Biol. 10 2003 980 (Pubitemid 37500485)
5. W.A. Warr ChEMBL. An interview with John Overington, team leader, chemogenomics at the European Bioinformatics Institute Outstation of the European Molecular Biology Laboratory (EMBL-EBI) J. Comput. Aided Mol. Des. 23 2009 195 198
6. N. Huang Benchmarking sets for molecular docking J. Med. Chem. 49 2006 6789 6801
7. S.G. Rohrer, and K. Baumann Maximum unbiased validation (MUV) data sets for virtual screening based on PubChem bioactivity data J. Chem. Inf. Model. 49 2009 169 184
8. P. Tiikkainen Critical comparison of virtual screening methods against the MUV data set J. Chem. Inf. Model. 49 2009 2168 2178
9. T.M. Steindl Parallel screening: a novel concept in pharmacophore modeling and virtual screening J. Chem. Inf. Model. 46 2006 2146 2157
10. T. Steindl Parallel screening and acitivity profiling with HIV protease inhibitor pharmacophore models J. Chem. Inf. Model. 47 2007 563 571
11. D. Schuster Development and validation of an in silico P450 profiler based on pharmacophore models Curr. Drug Discov. Technol. 3 2006 1 48
12. P. Markt Pharmacophore modeling and parallel screening for PPAR ligands J. Comput. Aided Mol. Des. 21 2007 575 590
13. G.M. Spitzer One concept, three implementations of 3D pharmacophore-based virtual screening: distinct coverage of chemical search space J. Chem. Inf. Model. 50 2010 1241 1247
14. M. Rella Structure-based pharmacophore design and virtual screening for novel angiotensin converting enzyme 2 inhibitors J. Chem. Inf. Model. 46 2006 708 716
15. D. Schuster The discovery of new 11β-hydroxysteroid dehydrogenase type 1 inhibitors by common feature pharmacophore modeling and virtual screening J. Med. Chem. 49 2006 3454 3466
16. S.K. Teo Thalidomide as a novel therapeutic agent: new uses for an old product Drug Discov. Today 10 2005 107 114
17. M. Campillos Drug target identification using side-effect similarity Science 321 2008 263 266
18. E. Dubus Drug repositioning using in silico compound profiling Future Med. Chem. 1 2009 1723 1736
19. Waltenberger, B. et al. (2010) Predicting cyclooxygenase activity by three-dimensional pharmacophoric profiling. Part II. Identification of enzyme inhibitors from Prasaplai, a Thai traditional medicine. Phytomed., e-pub ahead of print (Sept 20, 2010), doi:10.1016/j.phymed.2010.08.002
20. T.M. Ehrman In silico search for multi-target anti-inflammatories in Chinese herbs and formulas Bioorg. Med. Chem. 18 2010 2204 2218
21. J.M. Rollinger In silico target fishing for rationalized ligand discovery exemplified on constituents of Ruta graveolens Planta Med. 75 2009 195 204
22. J.M. Rollinger Accessing target information by virtual parallel screening - the impact on natural product research Phytochem. Lett. 2 2009 53 58
23. F. Celotti, and S. Laufer Anti-inflammatory drugs: new multitarget compounds to face an old problem. The dual inhibition concept Pharm. Res. 43 2001 429 436
24. S. Faivre New paradigms in anticancer therapy: targeting multiple signaling pathways with kinase inhibitors Semin. Oncol. 33 2006 407 420
25. S. Ekins In silico pharmacology for drug discovery: applications to targets and beyond Br. J. Pharmacol. 152 2007 21 37
26. D. Schuster, and G. Wolber Addressing drug safety by bioactivity profiling: panels of 3D pharmacophore models J. Scheiber, J. Jenkins, In silico Adverse Effect Prediction in Preclinical Drug Discovery 2010 John Wiley & Sons
27. H. van de Waterbeemd, and E. Gifford ADMET in silico modelling: towards prediction paradise? Nat. Rev. Drug Discov. 2 2003 192 204
28. M.J. Keiser Relating protein pharmacology by ligand chemistry Nat. Biotechnol. 25 2007 197 206
29. K. Kawai Predictive activity profiling of drugs by topological-fragment- spectra-based support vector machines J. Chem. Inf. Model. 48 2008 1152 1160
30. N. Wale, and G. Karypis Target fishing for chemical compounds using target-ligand activity data and ranking based methods J. Chem. Inf. Model. 49 2009 2190 2201
31. E. Kellenberger Ranking targets in structure-based virtual screening of three-dimensional protein libraries: methods and problems J. Chem. Inf. Model. 48 2008 1014 1025
32. Z.L. Ji In silico search of putative adverse drug reaction related proteins as a potential tool for facilitating drug adverse effect prediction Toxicol. Lett. 164 2006 104 112
33. Z. Gao PDTD: a web-accessible protein database for drug target identification BMC Bioinformatics 9 2008 104
34. D. Plouffe In silico activity profiling reveals the mechanism of action of antimalarials discovered in a high-throughput screen Proc. Natl. Acad. Sci. U. S. A. 105 2008 9059 9064
35. Milletti, F. and Vulpetti, A. (2010) Predicting polypharmacology by binding site similarity: from kinases to the protein universe. J. Chem. Inf. Model, e-pub ahead of print (Jun 4, 2010), doi:10.1021/ci1000532
36. R. Garcia-Serna, and J. Mestres Anticipating drug side effects by comparative pharmacology Expert Opin. Drug Metab. Toxicol. 6 2010 1253 1263
37. D. Vidal, and J. Mestres In silico receptorome screening of antipsychotic drugs Mol. Inf. 29 2010 543 551
38. A. Lagunin Multi-target natural products evaluation based on biological activity prediction with PASS Curr. Pharm. Des. 16 2010 1703 1717
39. D. Schuster, and G. Wolber Identification of bioactive natural products by pharmacophore-based virtual screening Curr. Pharm. Des. 16 2010 1666 1681
40. L.G. Nashev The UV-filter benzophenone-1 inhibits 17β-hydroxysteroid dehydrogenase type 3: virtual screening as a strategy to identify potential endocrine disrupting chemicals Biochem. Pharmacol. 79 2010 1189 1199