282 related articles for article (PubMed ID: 15341481)
1. Fuzzy pharmacophore models from molecular alignments for correlation-vector-based virtual screening.
Renner S; Schneider G
J Med Chem; 2004 Sep; 47(19):4653-64. PubMed ID: 15341481
[TBL] [Abstract][Full Text] [Related]
2. Extraction and visualization of potential pharmacophore points using support vector machines: application to ligand-based virtual screening for COX-2 inhibitors.
Franke L; Byvatov E; Werz O; Steinhilber D; Schneider P; Schneider G
J Med Chem; 2005 Nov; 48(22):6997-7004. PubMed ID: 16250658
[TBL] [Abstract][Full Text] [Related]
3. Combining ethnopharmacology and virtual screening for lead structure discovery: COX-inhibitors as application example.
Rollinger JM; Haupt S; Stuppner H; Langer T
J Chem Inf Comput Sci; 2004; 44(2):480-8. PubMed ID: 15032527
[TBL] [Abstract][Full Text] [Related]
4. Pharmacophore identification, in silico screening, and virtual library design for inhibitors of the human factor Xa.
Krovat EM; Frühwirth KH; Langer T
J Chem Inf Model; 2005; 45(1):146-59. PubMed ID: 15667140
[TBL] [Abstract][Full Text] [Related]
5. A virtual screening approach for thymidine monophosphate kinase inhibitors as antitubercular agents based on docking and pharmacophore models.
Gopalakrishnan B; Aparna V; Jeevan J; Ravi M; Desiraju GR
J Chem Inf Model; 2005; 45(4):1101-8. PubMed ID: 16045305
[TBL] [Abstract][Full Text] [Related]
6. Novel method for generating structure-based pharmacophores using energetic analysis.
Salam NK; Nuti R; Sherman W
J Chem Inf Model; 2009 Oct; 49(10):2356-68. PubMed ID: 19761201
[TBL] [Abstract][Full Text] [Related]
7. Sequence-derived three-dimensional pharmacophore models for G-protein-coupled receptors and their application in virtual screening.
Klabunde T; Giegerich C; Evers A
J Med Chem; 2009 May; 52(9):2923-32. PubMed ID: 19374402
[TBL] [Abstract][Full Text] [Related]
8. Novel technologies for virtual screening.
Lengauer T; Lemmen C; Rarey M; Zimmermann M
Drug Discov Today; 2004 Jan; 9(1):27-34. PubMed ID: 14761803
[TBL] [Abstract][Full Text] [Related]
9. Crystallographic study of inhibitors of tRNA-guanine transglycosylase suggests a new structure-based pharmacophore for virtual screening.
Brenk R; Meyer EA; Reuter K; Stubbs MT; Garcia GA; Diederich F; Klebe G
J Mol Biol; 2004 Apr; 338(1):55-75. PubMed ID: 15050823
[TBL] [Abstract][Full Text] [Related]
10. Multiple pharmacophore models combined with molecular docking: a reliable way for efficiently identifying novel PDE4 inhibitors with high structural diversity.
Chen Z; Tian G; Wang Z; Jiang H; Shen J; Zhu W
J Chem Inf Model; 2010 Apr; 50(4):615-25. PubMed ID: 20353193
[TBL] [Abstract][Full Text] [Related]
11. 3D QSAR pharmacophore based virtual screening and molecular docking for identification of potential HSP90 inhibitors.
Sakkiah S; Thangapandian S; John S; Kwon YJ; Lee KW
Eur J Med Chem; 2010 Jun; 45(6):2132-40. PubMed ID: 20206418
[TBL] [Abstract][Full Text] [Related]
12. Impact of conformational flexibility on three-dimensional similarity searching using correlation vectors.
Renner S; Schwab CH; Gasteiger J; Schneider G
J Chem Inf Model; 2006; 46(6):2324-32. PubMed ID: 17125176
[TBL] [Abstract][Full Text] [Related]
13. Virtual screening of biogenic amine-binding G-protein coupled receptors: comparative evaluation of protein- and ligand-based virtual screening protocols.
Evers A; Hessler G; Matter H; Klabunde T
J Med Chem; 2005 Aug; 48(17):5448-65. PubMed ID: 16107144
[TBL] [Abstract][Full Text] [Related]
14. Improving VEGFR-2 docking-based screening by pharmacophore postfiltering and similarity search postprocessing.
Planesas JM; Claramunt RM; Teixidó J; Borrell JI; Pérez-Nueno VI
J Chem Inf Model; 2011 Apr; 51(4):777-87. PubMed ID: 21417262
[TBL] [Abstract][Full Text] [Related]
15. Virtual screening and scaffold hopping based on GRID molecular interaction fields.
Ahlström MM; Ridderström M; Luthman K; Zamora I
J Chem Inf Model; 2005; 45(5):1313-23. PubMed ID: 16180908
[TBL] [Abstract][Full Text] [Related]
16. Structure-based pharmacophore design and virtual screening for novel angiotensin converting enzyme 2 inhibitors.
Rella M; Rushworth CA; Guy JL; Turner AJ; Langer T; Jackson RM
J Chem Inf Model; 2006; 46(2):708-16. PubMed ID: 16563001
[TBL] [Abstract][Full Text] [Related]
17. Influenza virus neuraminidase inhibitors: generation and comparison of structure-based and common feature pharmacophore hypotheses and their application in virtual screening.
Steindl T; Langer T
J Chem Inf Comput Sci; 2004; 44(5):1849-56. PubMed ID: 15446845
[TBL] [Abstract][Full Text] [Related]
18. Pruned receptor surface models and pharmacophores for three-dimensional database searching.
Sutherland JJ; O'Brien LA; Weaver DF
J Med Chem; 2004 Jul; 47(15):3777-87. PubMed ID: 15239656
[TBL] [Abstract][Full Text] [Related]
19. Efficient method for high-throughput virtual screening based on flexible docking: discovery of novel acetylcholinesterase inhibitors.
Mizutani MY; Itai A
J Med Chem; 2004 Sep; 47(20):4818-28. PubMed ID: 15369385
[TBL] [Abstract][Full Text] [Related]
20. Strategies for generating less toxic P-selectin inhibitors: pharmacophore modeling, virtual screening and counter pharmacophore screening to remove toxic hits.
Ananthula RS; Ravikumar M; Pramod AB; Madala KK; Mahmood SK
J Mol Graph Model; 2008 Nov; 27(4):546-57. PubMed ID: 18993099
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
