356 related articles for article (PubMed ID: 18993099)
1. 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]
2. Knowledge based identification of MAO-B selective inhibitors using pharmacophore and structure based virtual screening models.
Boppana K; Dubey PK; Jagarlapudi SA; Vadivelan S; Rambabu G
Eur J Med Chem; 2009 Sep; 44(9):3584-90. PubMed ID: 19321235
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. The discovery of novel vascular endothelial growth factor receptor tyrosine kinases inhibitors: pharmacophore modeling, virtual screening and docking studies.
Yu H; Wang Z; Zhang L; Zhang J; Huang Q
Chem Biol Drug Des; 2007 Mar; 69(3):204-11. PubMed ID: 17441906
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. Virtual screening against Mycobacterium tuberculosis dihydrofolate reductase: suggested workflow for compound prioritization using structure interaction fingerprints.
Kumar A; Siddiqi MI
J Mol Graph Model; 2008 Nov; 27(4):476-88. PubMed ID: 18829358
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Identification of potent urease inhibitors via ligand- and structure-based virtual screening and in vitro assays.
Khan KM; Wadood A; Ali M; Zia-Ullah ; Ul-Haq Z; Lodhi MA; Khan M; Perveen S; Choudhary MI
J Mol Graph Model; 2010 Jun; 28(8):792-8. PubMed ID: 20338793
[TBL] [Abstract][Full Text] [Related]
9. Pharmacophore-based virtual screening and docking studies on Hsp90 inhibitors.
Saxena S; Chaudhaery SS; Varshney K; Saxena AK
SAR QSAR Environ Res; 2010 Jul; 21(5-6):445-62. PubMed ID: 20818581
[TBL] [Abstract][Full Text] [Related]
10. Pharmacophore modeling and virtual screening studies to design some potential histone deacetylase inhibitors as new leads.
Vadivelan S; Sinha BN; Rambabu G; Boppana K; Jagarlapudi SA
J Mol Graph Model; 2008 Feb; 26(6):935-46. PubMed ID: 17707666
[TBL] [Abstract][Full Text] [Related]
11. Three-dimensional quantitative structure-activity relationship analysis of a set of Plasmodium falciparum dihydrofolate reductase inhibitors using a pharmacophore generation approach.
Parenti MD; Pacchioni S; Ferrari AM; Rastelli G
J Med Chem; 2004 Aug; 47(17):4258-67. PubMed ID: 15293997
[TBL] [Abstract][Full Text] [Related]
12. Structural analysis of carboline derivatives as inhibitors of MAPKAP K2 using 3D QSAR and docking studies.
Nayana RS; Bommisetty SK; Singh K; Bairy SK; Nunna S; Pramod A; Muttineni R
J Chem Inf Model; 2009 Jan; 49(1):53-67. PubMed ID: 19119997
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. Identification of novel serotonin 2C receptor ligands by sequential virtual screening.
Ahmed A; Choo H; Cho YS; Park WK; Pae AN
Bioorg Med Chem; 2009 Jul; 17(13):4559-68. PubMed ID: 19464901
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. 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]
17. On the value of homology models for virtual screening: discovering hCXCR3 antagonists by pharmacophore-based and structure-based approaches.
Huang D; Gu Q; Ge H; Ye J; Salam NK; Hagler A; Chen H; Xu J
J Chem Inf Model; 2012 May; 52(5):1356-66. PubMed ID: 22545675
[TBL] [Abstract][Full Text] [Related]
18. Pharmacophore modeling and virtual screening for designing potential 5-lipoxygenase inhibitors.
Aparoy P; Kumar Reddy K; Kalangi SK; Chandramohan Reddy T; Reddanna P
Bioorg Med Chem Lett; 2010 Feb; 20(3):1013-8. PubMed ID: 20045317
[TBL] [Abstract][Full Text] [Related]
19. Pharmacophore modeling and virtual screening for designing potential PLK1 inhibitors.
Wang HY; Cao ZX; Li LL; Jiang PD; Zhao YL; Luo SD; Yang L; Wei YQ; Yang SY
Bioorg Med Chem Lett; 2008 Sep; 18(18):4972-7. PubMed ID: 18762425
[TBL] [Abstract][Full Text] [Related]
20. A specific pharmacophore model of Aurora B kinase inhibitors and virtual screening studies based on it.
Wang HY; Li LL; Cao ZX; Luo SD; Wei YQ; Yang SY
Chem Biol Drug Des; 2009 Jan; 73(1):115-26. PubMed ID: 19152640
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]