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Pubmed for Handhelds

PUBMED FOR HANDHELDS

Journal Abstract Search

122 related items for PubMed ID: 16995738

  • 1.
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  • 3. Improvement of protein-compound docking scores by using amino-acid sequence similarities of proteins.
    Fukunishi Y, Nakamura H.
    J Chem Inf Model; 2008 Jan; 48(1):148-56. PubMed ID: 18166019
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  • 4. 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
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  • 5. 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 Sep; 46(6):2610-22. PubMed ID: 17125201
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  • 6. Virtual high-throughput screening of molecular databases.
    Seifert MH, Kraus J, Kramer B.
    Curr Opin Drug Discov Devel; 2007 May; 10(3):298-307. PubMed ID: 17554856
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  • 7. A large-scale computational approach to drug repositioning.
    Li YY, An J, Jones SJ.
    Genome Inform; 2006 May; 17(2):239-47. PubMed ID: 17503396
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  • 8. 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
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  • 9. 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
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  • 10. Finding the target after screening the phenotype.
    Hart CP.
    Drug Discov Today; 2005 Apr 01; 10(7):513-9. PubMed ID: 15809197
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  • 11. Ligand-target interaction-based weighting of substructures for virtual screening.
    Crisman TJ, Sisay MT, Bajorath J.
    J Chem Inf Model; 2008 Oct 01; 48(10):1955-64. PubMed ID: 18821751
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  • 12. Lead finder: an approach to improve accuracy of protein-ligand docking, binding energy estimation, and virtual screening.
    Stroganov OV, Novikov FN, Stroylov VS, Kulkov V, Chilov GG.
    J Chem Inf Model; 2008 Dec 01; 48(12):2371-85. PubMed ID: 19007114
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  • 13. A new method for in-silico drug screening and similarity search using molecular dynamics maximum volume overlap (MD-MVO) method.
    Fukunishi Y, Nakamura H.
    J Mol Graph Model; 2009 Jan 01; 27(5):628-36. PubMed ID: 19046907
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  • 14. DrugBank: a comprehensive resource for in silico drug discovery and exploration.
    Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, Chang Z, Woolsey J.
    Nucleic Acids Res; 2006 Jan 01; 34(Database issue):D668-72. PubMed ID: 16381955
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  • 15. Structure-based drug screening and ligand-based drug screening with machine learning.
    Fukunishi Y.
    Comb Chem High Throughput Screen; 2009 May 01; 12(4):397-408. PubMed ID: 19442067
    [Abstract] [Full Text] [Related]

  • 16. Introduction of an information-theoretic method to predict recovery rates of active compounds for Bayesian in silico screening: theory and screening trials.
    Vogt M, Bajorath J.
    J Chem Inf Model; 2007 May 01; 47(2):337-41. PubMed ID: 17302401
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  • 17. 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 01; 49(3):623-33. PubMed ID: 19231809
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  • 18. Data mining a small molecule drug screening representative subset from NIH PubChem.
    Xie XQ, Chen JZ.
    J Chem Inf Model; 2008 Mar 01; 48(3):465-75. PubMed ID: 18302356
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  • 19. In silico multi-filter screening approaches for developing novel beta-secretase inhibitors.
    Fujimoto T, Matsushita Y, Gouda H, Yamaotsu N, Hirono S.
    Bioorg Med Chem Lett; 2008 May 01; 18(9):2771-5. PubMed ID: 18434150
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  • 20. Novel method for generating structure-based pharmacophores using energetic analysis.
    Salam NK, Nuti R, Sherman W.
    J Chem Inf Model; 2009 Oct 01; 49(10):2356-68. PubMed ID: 19761201
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