These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
290 related articles for article (PubMed ID: 18281962)
21. 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]
23. Towards more accurate pharmacophore modeling: Multicomplex-based comprehensive pharmacophore map and most-frequent-feature pharmacophore model of CDK2. Zou J; Xie HZ; Yang SY; Chen JJ; Ren JX; Wei YQ J Mol Graph Model; 2008 Nov; 27(4):430-8. PubMed ID: 18786843 [TBL] [Abstract][Full Text] [Related]
24. New 4-point pharmacophore method for molecular similarity and diversity applications: overview of the method and applications, including a novel approach to the design of combinatorial libraries containing privileged substructures. Mason JS; Morize I; Menard PR; Cheney DL; Hulme C; Labaudiniere RF J Med Chem; 1999 Aug; 42(17):3251-64. PubMed ID: 10464012 [TBL] [Abstract][Full Text] [Related]
25. How similar are similarity searching methods? A principal component analysis of molecular descriptor space. Bender A; Jenkins JL; Scheiber J; Sukuru SC; Glick M; Davies JW J Chem Inf Model; 2009 Jan; 49(1):108-19. PubMed ID: 19123924 [TBL] [Abstract][Full Text] [Related]
26. Atomic property fields: generalized 3D pharmacophoric potential for automated ligand superposition, pharmacophore elucidation and 3D QSAR. Totrov M Chem Biol Drug Des; 2008 Jan; 71(1):15-27. PubMed ID: 18069986 [TBL] [Abstract][Full Text] [Related]
27. A knowledge-based weighting approach to ligand-based virtual screening. Stiefl N; Zaliani A J Chem Inf Model; 2006; 46(2):587-96. PubMed ID: 16562987 [TBL] [Abstract][Full Text] [Related]
28. 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]
29. Bayesian screening for active compounds in high-dimensional chemical spaces combining property descriptors and molecular fingerprints. Vogt M; Bajorath J Chem Biol Drug Des; 2008 Jan; 71(1):8-14. PubMed ID: 18069988 [TBL] [Abstract][Full Text] [Related]
30. Mini-fingerprints for virtual screening: design principles and generation of novel prototypes based on information theory. Xue L; Godden JW; Bajorath J SAR QSAR Environ Res; 2003 Feb; 14(1):27-40. PubMed ID: 12688414 [TBL] [Abstract][Full Text] [Related]
31. Generation, validation, and utilization of a three-dimensional pharmacophore model for EP3 antagonists. Mishra RK; Singh J J Chem Inf Model; 2010 Aug; 50(8):1502-9. PubMed ID: 20726604 [TBL] [Abstract][Full Text] [Related]
32. Hot-spots-guided receptor-based pharmacophores (HS-Pharm): a knowledge-based approach to identify ligand-anchoring atoms in protein cavities and prioritize structure-based pharmacophores. Barillari C; Marcou G; Rognan D J Chem Inf Model; 2008 Jul; 48(7):1396-410. PubMed ID: 18570371 [TBL] [Abstract][Full Text] [Related]
33. The use of protein-ligand interaction fingerprints in docking. Brewerton SC Curr Opin Drug Discov Devel; 2008 May; 11(3):356-64. PubMed ID: 18428089 [TBL] [Abstract][Full Text] [Related]
34. SitePrint: three-dimensional pharmacophore descriptors derived from protein binding sites for family based active site analysis, classification, and drug design. Arnold JR; Burdick KW; Pegg SC; Toba S; Lamb ML; Kuntz ID J Chem Inf Comput Sci; 2004; 44(6):2190-8. PubMed ID: 15554689 [TBL] [Abstract][Full Text] [Related]
35. Using pharmacophore models to gain insight into structural binding and virtual screening: an application study with CDK2 and human DHFR. Toba S; Srinivasan J; Maynard AJ; Sutter J J Chem Inf Model; 2006; 46(2):728-35. PubMed ID: 16563003 [TBL] [Abstract][Full Text] [Related]
36. 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]
37. 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]
38. 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]
39. Virtual screening for Raf-1 kinase inhibitors based on pharmacophore model of substituted ureas. Li HF; Lu T; Zhu T; Jiang YJ; Rao SS; Hu LY; Xin BT; Chen YD Eur J Med Chem; 2009 Mar; 44(3):1240-9. PubMed ID: 18947905 [TBL] [Abstract][Full Text] [Related]
40. HPPD: ligand- and target-based virtual screening on a herbicide target. López-Ramos M; Perruccio F J Chem Inf Model; 2010 May; 50(5):801-14. PubMed ID: 20359237 [TBL] [Abstract][Full Text] [Related] [Previous] [Next] [New Search]