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.
230 related articles for article (PubMed ID: 17097188)
1. Pharmacophore-based discovery of ligands for drug transporters. Chang C; Ekins S; Bahadduri P; Swaan PW Adv Drug Deliv Rev; 2006 Nov; 58(12-13):1431-50. PubMed ID: 17097188 [TBL] [Abstract][Full Text] [Related]
2. In vitro and pharmacophore-based discovery of novel hPEPT1 inhibitors. Ekins S; Johnston JS; Bahadduri P; D'Souza VM; Ray A; Chang C; Swaan PW Pharm Res; 2005 Apr; 22(4):512-7. PubMed ID: 15846457 [TBL] [Abstract][Full Text] [Related]
3. New features that improve the pharmacophore tools from Accelrys. Sutter J; Li J; Maynard AJ; Goupil A; Luu T; Nadassy K Curr Comput Aided Drug Des; 2011 Sep; 7(3):173-80. PubMed ID: 21726193 [TBL] [Abstract][Full Text] [Related]
4. Chemical feature-based pharmacophores and virtual library screening for discovery of new leads. Langer T; Krovat EM Curr Opin Drug Discov Devel; 2003 May; 6(3):370-6. PubMed ID: 12833670 [TBL] [Abstract][Full Text] [Related]
5. Molecular modeling of the three-dimensional structure of dopamine 3 (D3) subtype receptor: discovery of novel and potent D3 ligands through a hybrid pharmacophore- and structure-based database searching approach. Varady J; Wu X; Fang X; Min J; Hu Z; Levant B; Wang S J Med Chem; 2003 Oct; 46(21):4377-92. PubMed ID: 14521403 [TBL] [Abstract][Full Text] [Related]
6. Exploration of the structural requirements of HIV-protease inhibitors using pharmacophore, virtual screening and molecular docking approaches for lead identification. Islam MA; Pillay TS J Mol Graph Model; 2015 Mar; 56():20-30. PubMed ID: 25541527 [TBL] [Abstract][Full Text] [Related]
7. Protein-ligand-based pharmacophores: generation and utility assessment in computational ligand profiling. Meslamani J; Li J; Sutter J; Stevens A; Bertrand HO; Rognan D J Chem Inf Model; 2012 Apr; 52(4):943-55. PubMed ID: 22480372 [TBL] [Abstract][Full Text] [Related]
8. Modeling of the intestinal peptide transporter hPepT1 and analysis of its transport capacities by docking and pharmacophore mapping. Pedretti A; De Luca L; Marconi C; Negrisoli G; Aldini G; Vistoli G ChemMedChem; 2008 Dec; 3(12):1913-21. PubMed ID: 18979492 [TBL] [Abstract][Full Text] [Related]
9. New serotonin 5-HT(6) ligands from common feature pharmacophore hypotheses. Kim HJ; Doddareddy MR; Choo H; Cho YS; No KT; Park WK; Pae AN J Chem Inf Model; 2008 Jan; 48(1):197-206. PubMed ID: 18044950 [TBL] [Abstract][Full Text] [Related]
10. Pharmacophore design and database searching for selective monoamine neurotransmitter transporter ligands. Macdougall IJ; Griffith R J Mol Graph Model; 2008 Apr; 26(7):1113-24. PubMed ID: 18023378 [TBL] [Abstract][Full Text] [Related]
11. A review of ligand-based virtual screening web tools and screening algorithms in large molecular databases in the age of big data. Banegas-Luna AJ; Cerón-Carrasco JP; Pérez-Sánchez H Future Med Chem; 2018 Nov; 10(22):2641-2658. PubMed ID: 30499744 [TBL] [Abstract][Full Text] [Related]
12. Pharmacophore modeling in drug discovery and development: an overview. Khedkar SA; Malde AK; Coutinho EC; Srivastava S Med Chem; 2007 Mar; 3(2):187-97. PubMed ID: 17348856 [TBL] [Abstract][Full Text] [Related]
13. The discovery of novel β-secretase inhibitors: pharmacophore modeling, virtual screening, and docking studies. Niu Y; Ma C; Jin H; Xu F; Gao H; Liu P; Li Y; Wang C; Yang G; Xu P Chem Biol Drug Des; 2012 Jun; 79(6):972-80. PubMed ID: 22381116 [TBL] [Abstract][Full Text] [Related]
14. Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Yang SY Drug Discov Today; 2010 Jun; 15(11-12):444-50. PubMed ID: 20362693 [TBL] [Abstract][Full Text] [Related]
15. 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]
16. Discovery of non-glycoside sodium-dependent glucose co-transporter 2 (SGLT2) inhibitors by ligand-based virtual screening. Wu JS; Peng YH; Wu JM; Hsieh CJ; Wu SH; Coumar MS; Song JS; Lee JC; Tsai CH; Chen CT; Liu YW; Chao YS; Wu SY J Med Chem; 2010 Dec; 53(24):8770-4. PubMed ID: 21090651 [TBL] [Abstract][Full Text] [Related]
17. Potential virtual lead identification in the discovery of renin inhibitors: application of ligand and structure-based pharmacophore modeling approaches. Thangapandian S; John S; Sakkiah S; Lee KW Eur J Med Chem; 2011 Jun; 46(6):2469-76. PubMed ID: 21497958 [TBL] [Abstract][Full Text] [Related]
18. Discovery of new renin inhibitory leads via sequential pharmacophore modeling, QSAR analysis, in silico screening and in vitro evaluation. Al-Nadaf AH; Taha MO J Mol Graph Model; 2011 Apr; 29(6):843-64. PubMed ID: 21376648 [TBL] [Abstract][Full Text] [Related]
19. Ligand and structure based pharmacophore modeling to facilitate novel histone deacetylase 8 inhibitor design. Thangapandian S; John S; Sakkiah S; Lee KW Eur J Med Chem; 2010 Oct; 45(10):4409-17. PubMed ID: 20656379 [TBL] [Abstract][Full Text] [Related]
20. Ligand and structure-based approaches for the identification of SIRT1 activators. Vyas VK; Goel A; Ghate M; Patel P Chem Biol Interact; 2015 Feb; 228():9-17. PubMed ID: 25595223 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]