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Pubmed for Handhelds

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Journal Abstract Search

159 related items for PubMed ID: 26958710

  • 21. Integrating sampling techniques and inverse virtual screening: toward the discovery of artificial peptide-based receptors for ligands.
    Pérez GM, Salomón LA, Montero-Cabrera LA, de la Vega JM, Mascini M.
    Mol Divers; 2016 May; 20(2):421-38. PubMed ID: 26553204
    [Abstract] [Full Text] [Related]

  • 22. Machine Learning Consensus Scoring Improves Performance Across Targets in Structure-Based Virtual Screening.
    Ericksen SS, Wu H, Zhang H, Michael LA, Newton MA, Hoffmann FM, Wildman SA.
    J Chem Inf Model; 2017 Jul 24; 57(7):1579-1590. PubMed ID: 28654262
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  • 23. Recipes for the selection of experimental protein conformations for virtual screening.
    Rueda M, Bottegoni G, Abagyan R.
    J Chem Inf Model; 2010 Jan 24; 50(1):186-93. PubMed ID: 20000587
    [Abstract] [Full Text] [Related]

  • 24. Toward fully automated high performance computing drug discovery: a massively parallel virtual screening pipeline for docking and molecular mechanics/generalized Born surface area rescoring to improve enrichment.
    Zhang X, Wong SE, Lightstone FC.
    J Chem Inf Model; 2014 Jan 27; 54(1):324-37. PubMed ID: 24358939
    [Abstract] [Full Text] [Related]

  • 25. A prospective cross-screening study on G-protein-coupled receptors: lessons learned in virtual compound library design.
    Sanders MP, Roumen L, van der Horst E, Lane JR, Vischer HF, van Offenbeek J, de Vries H, Verhoeven S, Chow KY, Verkaar F, Beukers MW, McGuire R, Leurs R, Ijzerman AP, de Vlieg J, de Esch IJ, Zaman GJ, Klomp JP, Bender A, de Graaf C.
    J Med Chem; 2012 Jun 14; 55(11):5311-25. PubMed ID: 22563707
    [Abstract] [Full Text] [Related]

  • 26. Comparison of several molecular docking programs: pose prediction and virtual screening accuracy.
    Cross JB, Thompson DC, Rai BK, Baber JC, Fan KY, Hu Y, Humblet C.
    J Chem Inf Model; 2009 Jun 14; 49(6):1455-74. PubMed ID: 19476350
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  • 27. Machine learning in computational docking.
    Khamis MA, Gomaa W, Ahmed WF.
    Artif Intell Med; 2015 Mar 14; 63(3):135-52. PubMed ID: 25724101
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  • 28. Highly specific and sensitive pharmacophore model for identifying CXCR4 antagonists. Comparison with docking and shape-matching virtual screening performance.
    Karaboga AS, Planesas JM, Petronin F, Teixidó J, Souchet M, Pérez-Nueno VI.
    J Chem Inf Model; 2013 May 24; 53(5):1043-56. PubMed ID: 23577723
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  • 29. Protein tyrosine phosphatases: Ligand interaction analysis and optimisation of virtual screening.
    Ghattas MA, Atatreh N, Bichenkova EV, Bryce RA.
    J Mol Graph Model; 2014 Jul 24; 52():114-23. PubMed ID: 25038507
    [Abstract] [Full Text] [Related]

  • 30. Target-specific native/decoy pose classifier improves the accuracy of ligand ranking in the CSAR 2013 benchmark.
    Fourches D, Politi R, Tropsha A.
    J Chem Inf Model; 2015 Jan 26; 55(1):63-71. PubMed ID: 25521713
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  • 31. CRDOCK: an ultrafast multipurpose protein-ligand docking tool.
    Cortés Cabrera Á, Klett J, Dos Santos HG, Perona A, Gil-Redondo R, Francis SM, Priego EM, Gago F, Morreale A.
    J Chem Inf Model; 2012 Aug 27; 52(8):2300-9. PubMed ID: 22764680
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  • 32. Improved Method of Structure-Based Virtual Screening via Interaction-Energy-Based Learning.
    Yasuo N, Sekijima M.
    J Chem Inf Model; 2019 Mar 25; 59(3):1050-1061. PubMed ID: 30808172
    [Abstract] [Full Text] [Related]

  • 33. Toward a benchmarking data set able to evaluate ligand- and structure-based virtual screening using public HTS data.
    Lindh M, Svensson F, Schaal W, Zhang J, Sköld C, Brandt P, Karlén A.
    J Chem Inf Model; 2015 Feb 23; 55(2):343-53. PubMed ID: 25564966
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  • 34. Reliability analysis and optimization of the consensus docking approach for the development of virtual screening studies.
    Poli G, Martinelli A, Tuccinardi T.
    J Enzyme Inhib Med Chem; 2016 Feb 23; 31(sup2):167-173. PubMed ID: 27311630
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  • 35. FlexAID: Revisiting Docking on Non-Native-Complex Structures.
    Gaudreault F, Najmanovich RJ.
    J Chem Inf Model; 2015 Jul 27; 55(7):1323-36. PubMed ID: 26076070
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  • 36. Development of New Methods Needs Proper Evaluation-Benchmarking Sets for Machine Learning Experiments for Class A GPCRs.
    Leśniak D, Podlewska S, Jastrzębski S, Sieradzki I, Bojarski AJ, Tabor J.
    J Chem Inf Model; 2019 Dec 23; 59(12):4974-4992. PubMed ID: 31604014
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  • 37. Extended template-based modeling and evaluation method using consensus of binding mode of GPCRs for virtual screening.
    Sato M, Hirokawa T.
    J Chem Inf Model; 2014 Nov 24; 54(11):3153-61. PubMed ID: 25350693
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  • 38. Structure-based virtual screening approach for discovery of covalently bound ligands.
    Toledo Warshaviak D, Golan G, Borrelli KW, Zhu K, Kalid O.
    J Chem Inf Model; 2014 Jul 28; 54(7):1941-50. PubMed ID: 24932913
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  • 39. Evaluation and optimization of virtual screening workflows with DEKOIS 2.0--a public library of challenging docking benchmark sets.
    Bauer MR, Ibrahim TM, Vogel SM, Boeckler FM.
    J Chem Inf Model; 2013 Jun 24; 53(6):1447-62. PubMed ID: 23705874
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  • 40. Virtual screening of biogenic amine-binding G-protein coupled receptors: comparative evaluation of protein- and ligand-based virtual screening protocols.
    Evers A, Hessler G, Matter H, Klabunde T.
    J Med Chem; 2005 Aug 25; 48(17):5448-65. PubMed ID: 16107144
    [Abstract] [Full Text] [Related]

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