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.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

79 related articles for article (PubMed ID: 20578712)

  • 1. Selection of in silico drug screening results by using universal active probes (UAPs).
    Fukunishi Y; Ohno K; Orita M; Nakamura H
    J Chem Inf Model; 2010 Jul; 50(7):1233-40. PubMed ID: 20578712
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Selection of in silico drug screening results for G-protein-coupled receptors by using universal active probes.
    Wada M; Kanamori E; Nakamura H; Fukunishi Y
    J Chem Inf Model; 2011 Sep; 51(9):2398-407. PubMed ID: 21848279
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Multiple target screening method for robust and accurate in silico ligand screening.
    Fukunishi Y; Mikami Y; Kubota S; Nakamura H
    J Mol Graph Model; 2006 Sep; 25(1):61-70. PubMed ID: 16376595
    [TBL] [Abstract][Full Text] [Related]  

  • 4. In-silico drug screening method based on the protein-compound affinity matrix using the factor selection technique.
    Murali S; Hojo S; Tsujishita H; Nakamura H; Fukunishi Y
    Eur J Med Chem; 2007 Jul; 42(7):966-76. PubMed ID: 17307278
    [TBL] [Abstract][Full Text] [Related]  

  • 5. In silico fragment screening by replica generation (FSRG) method for fragment-based drug design.
    Fukunishi Y; Mashimo T; Orita M; Ohno K; Nakamura H
    J Chem Inf Model; 2009 Apr; 49(4):925-33. PubMed ID: 19354203
    [TBL] [Abstract][Full Text] [Related]  

  • 6. An efficient in silico screening method based on the protein-compound affinity matrix and its application to the design of a focused library for cytochrome P450 (CYP) ligands.
    Fukunishi Y; Hojo S; Nakamura H
    J Chem Inf Model; 2006; 46(6):2610-22. PubMed ID: 17125201
    [TBL] [Abstract][Full Text] [Related]  

  • 7. SeleX-CS: a new consensus scoring algorithm for hit discovery and lead optimization.
    Bar-Haim S; Aharon A; Ben-Moshe T; Marantz Y; Senderowitz H
    J Chem Inf Model; 2009 Mar; 49(3):623-33. PubMed ID: 19231809
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Virtual screen for ligands of orphan G protein-coupled receptors.
    Bock JR; Gough DA
    J Chem Inf Model; 2005; 45(5):1402-14. PubMed ID: 16180917
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Structure-based approach to pharmacophore identification, in silico screening, and three-dimensional quantitative structure-activity relationship studies for inhibitors of Trypanosoma cruzi dihydrofolate reductase function.
    Schormann N; Senkovich O; Walker K; Wright DL; Anderson AC; Rosowsky A; Ananthan S; Shinkre B; Velu S; Chattopadhyay D
    Proteins; 2008 Dec; 73(4):889-901. PubMed ID: 18536013
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Similarities among receptor pockets and among compounds: analysis and application to in silico ligand screening.
    Fukunishi Y; Mikami Y; Nakamura H
    J Mol Graph Model; 2005 Sep; 24(1):34-45. PubMed ID: 15950507
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Similarity metrics for ligands reflecting the similarity of the target proteins.
    Schuffenhauer A; Floersheim P; Acklin P; Jacoby E
    J Chem Inf Comput Sci; 2003; 43(2):391-405. PubMed ID: 12653501
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Initial compound selection for sequential screening.
    Young SS; Lam RL; Welch WJ
    Curr Opin Drug Discov Devel; 2002 May; 5(3):422-7. PubMed ID: 12058618
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Finding ligands for G protein-coupled receptors based on the protein-compound affinity matrix.
    Fukunishi Y; Kubota S; Nakamura H
    J Mol Graph Model; 2007 Jan; 25(5):633-43. PubMed ID: 16777448
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Scoring ligand similarity in structure-based virtual screening.
    Zavodszky MI; Rohatgi A; Van Voorst JR; Yan H; Kuhn LA
    J Mol Recognit; 2009; 22(4):280-92. PubMed ID: 19235177
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Consensus scoring with feature selection for structure-based virtual screening.
    Teramoto R; Fukunishi H
    J Chem Inf Model; 2008 Feb; 48(2):288-95. PubMed ID: 18229906
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Combining machine learning and pharmacophore-based interaction fingerprint for in silico screening.
    Sato T; Honma T; Yokoyama S
    J Chem Inf Model; 2010 Jan; 50(1):170-85. PubMed ID: 20038188
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Virtual screening - what does it give us?
    Köppen H
    Curr Opin Drug Discov Devel; 2009 May; 12(3):397-407. PubMed ID: 19396741
    [TBL] [Abstract][Full Text] [Related]  

  • 18. In silico chemical library screening and experimental validation of a novel 9-aminoacridine based lead-inhibitor of human S-adenosylmethionine decarboxylase.
    Brooks WH; McCloskey DE; Daniel KG; Ealick SE; Secrist JA; Waud WR; Pegg AE; Guida WC
    J Chem Inf Model; 2007; 47(5):1897-905. PubMed ID: 17676832
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Evaluation of virtual screening as a tool for chemical genetic applications.
    Campagna-Slater V; Schapira M
    J Chem Inf Model; 2009 Sep; 49(9):2082-91. PubMed ID: 19702241
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Virtual screening strategies in drug discovery.
    McInnes C
    Curr Opin Chem Biol; 2007 Oct; 11(5):494-502. PubMed ID: 17936059
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 4.