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Pubmed for Handhelds

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Journal Abstract Search

204 related items for PubMed ID: 28678484

  • 21. Characterization of small molecule binding. I. Accurate identification of strong inhibitors in virtual screening.
    Ding B, Wang J, Li N, Wang W.
    J Chem Inf Model; 2013 Jan 28; 53(1):114-22. PubMed ID: 23259763
    [Abstract] [Full Text] [Related]

  • 22. 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]

  • 23. Minimizing false positives in kinase virtual screens.
    Perola E.
    Proteins; 2006 Aug 01; 64(2):422-35. PubMed ID: 16708364
    [Abstract] [Full Text] [Related]

  • 24. Hidden bias in the DUD-E dataset leads to misleading performance of deep learning in structure-based virtual screening.
    Chen L, Cruz A, Ramsey S, Dickson CJ, Duca JS, Hornak V, Koes DR, Kurtzman T.
    PLoS One; 2019 Aug 01; 14(8):e0220113. PubMed ID: 31430292
    [Abstract] [Full Text] [Related]

  • 25. Docking Score ML: Target-Specific Machine Learning Models Improving Docking-Based Virtual Screening in 155 Targets.
    Liu H, Hu B, Chen P, Wang X, Wang H, Wang S, Wang J, Lin B, Cheng M.
    J Chem Inf Model; 2024 Jul 22; 64(14):5413-5426. PubMed ID: 38958413
    [Abstract] [Full Text] [Related]

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  • 27. CSAR Benchmark of Flexible MedusaDock in Affinity Prediction and Nativelike Binding Pose Selection.
    Nedumpully-Govindan P, Jemec DB, Ding F.
    J Chem Inf Model; 2016 Jun 27; 56(6):1042-52. PubMed ID: 26252196
    [Abstract] [Full Text] [Related]

  • 28. LS-align: an atom-level, flexible ligand structural alignment algorithm for high-throughput virtual screening.
    Hu J, Liu Z, Yu DJ, Zhang Y.
    Bioinformatics; 2018 Jul 01; 34(13):2209-2218. PubMed ID: 29462237
    [Abstract] [Full Text] [Related]

  • 29. Fragment-Based Analysis of Ligand Dockings Improves Classification of Actives.
    Belew RK, Forli S, Goodsell DS, O'Donnell TJ, Olson AJ.
    J Chem Inf Model; 2016 Aug 22; 56(8):1597-607. PubMed ID: 27384036
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  • 31. The Development of Target-Specific Pose Filter Ensembles To Boost Ligand Enrichment for Structure-Based Virtual Screening.
    Xia J, Hsieh JH, Hu H, Wu S, Wang XS.
    J Chem Inf Model; 2017 Jun 26; 57(6):1414-1425. PubMed ID: 28511009
    [Abstract] [Full Text] [Related]

  • 32. Forging the Basis for Developing Protein-Ligand Interaction Scoring Functions.
    Liu Z, Su M, Han L, Liu J, Yang Q, Li Y, Wang R.
    Acc Chem Res; 2017 Feb 21; 50(2):302-309. PubMed ID: 28182403
    [Abstract] [Full Text] [Related]

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  • 34. 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 21; 49(6):1455-74. PubMed ID: 19476350
    [Abstract] [Full Text] [Related]

  • 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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  • 37. A comparative assessment of ranking accuracies of conventional and machine-learning-based scoring functions for protein-ligand binding affinity prediction.
    Ashtawy HM, Mahapatra NR.
    IEEE/ACM Trans Comput Biol Bioinform; 2012 Jul 27; 9(5):1301-13. PubMed ID: 22411892
    [Abstract] [Full Text] [Related]

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  • 39. A pharmacophore-based evolutionary approach for screening selective estrogen receptor modulators.
    Yang JM, Shen TW.
    Proteins; 2005 May 01; 59(2):205-20. PubMed ID: 15726586
    [Abstract] [Full Text] [Related]

  • 40. 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
    [Abstract] [Full Text] [Related]

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