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Journal Abstract Search

510 related items for PubMed ID: 27491652

  • 21. Automated Inference of Chemical Discriminants of Biological Activity.
    Raschka S, Scott AM, Huertas M, Li W, Kuhn LA.
    Methods Mol Biol; 2018; 1762():307-338. PubMed ID: 29594779
    [Abstract] [Full Text] [Related]

  • 22. G-protein-coupled receptor-focused drug discovery using a target class platform approach.
    Heilker R, Wolff M, Tautermann CS, Bieler M.
    Drug Discov Today; 2009 Mar; 14(5-6):231-40. PubMed ID: 19121411
    [Abstract] [Full Text] [Related]

  • 23. Modeling of p38 mitogen-activated protein kinase inhibitors using the Catalyst HypoGen and k-nearest neighbor QSAR methods.
    Xiao Z, Varma S, Xiao YD, Tropsha A.
    J Mol Graph Model; 2004 Oct; 23(2):129-38. PubMed ID: 15363455
    [Abstract] [Full Text] [Related]

  • 24. Ligand and decoy sets for docking to G protein-coupled receptors.
    Gatica EA, Cavasotto CN.
    J Chem Inf Model; 2012 Jan 23; 52(1):1-6. PubMed ID: 22168315
    [Abstract] [Full Text] [Related]

  • 25. G-protein coupled receptors virtual screening using genetic algorithm focused chemical space.
    Sage C, Wang R, Jones G.
    J Chem Inf Model; 2011 Aug 22; 51(8):1754-61. PubMed ID: 21761904
    [Abstract] [Full Text] [Related]

  • 26. Emerging Approaches to GPCR Ligand Screening for Drug Discovery.
    Kumari P, Ghosh E, Shukla AK.
    Trends Mol Med; 2015 Nov 22; 21(11):687-701. PubMed ID: 26481827
    [Abstract] [Full Text] [Related]

  • 27. Rough set-based proteochemometrics modeling of G-protein-coupled receptor-ligand interactions.
    Strömbergsson H, Prusis P, Midelfart H, Lapinsh M, Wikberg JE, Komorowski J.
    Proteins; 2006 Apr 01; 63(1):24-34. PubMed ID: 16435365
    [Abstract] [Full Text] [Related]

  • 28. Ligand-based computer-aided discovery of tyrosinase inhibitors. Applications of the TOMOCOMD-CARDD method to the elucidation of new compounds.
    Marrero-Ponce Y, Casañola-Martín GM, Khan MT, Torrens F, Rescigno A, Abad C.
    Curr Pharm Des; 2010 Apr 01; 16(24):2601-24. PubMed ID: 20642427
    [Abstract] [Full Text] [Related]

  • 29. Ligand-based virtual screening and in silico design of new antimalarial compounds using nonstochastic and stochastic total and atom-type quadratic maps.
    Marrero-Ponce Y, Iyarreta-Veitía M, Montero-Torres A, Romero-Zaldivar C, Brandt CA, Avila PE, Kirchgatter K, Machado Y.
    J Chem Inf Model; 2005 Apr 01; 45(4):1082-100. PubMed ID: 16045304
    [Abstract] [Full Text] [Related]

  • 30. Novel inhibitors of human histone deacetylase (HDAC) identified by QSAR modeling of known inhibitors, virtual screening, and experimental validation.
    Tang H, Wang XS, Huang XP, Roth BL, Butler KV, Kozikowski AP, Jung M, Tropsha A.
    J Chem Inf Model; 2009 Feb 01; 49(2):461-76. PubMed ID: 19182860
    [Abstract] [Full Text] [Related]

  • 31. Evaluation of QSAR Equations for Virtual Screening.
    Spiegel J, Senderowitz H.
    Int J Mol Sci; 2020 Oct 22; 21(21):. PubMed ID: 33105703
    [Abstract] [Full Text] [Related]

  • 32.
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  • 33. Characterizing common substructures of ligands for GPCR protein subfamilies.
    Erguner B, Hattori M, Goto S, Kanehisa M.
    Genome Inform; 2010 Oct 22; 24():31-41. PubMed ID: 22081587
    [Abstract] [Full Text] [Related]

  • 34. Antitumor agents 252. Application of validated QSAR models to database mining: discovery of novel tylophorine derivatives as potential anticancer agents.
    Zhang S, Wei L, Bastow K, Zheng W, Brossi A, Lee KH, Tropsha A.
    J Comput Aided Mol Des; 2007 Oct 22; 21(1-3):97-112. PubMed ID: 17340042
    [Abstract] [Full Text] [Related]

  • 35. Ligand identification for G-protein-coupled receptors: a lead generation perspective.
    Bleicher KH, Green LG, Martin RE, Rogers-Evans M.
    Curr Opin Chem Biol; 2004 Jun 22; 8(3):287-96. PubMed ID: 15183327
    [Abstract] [Full Text] [Related]

  • 36. Toward the prediction of class I and II mouse major histocompatibility complex-peptide-binding affinity: in silico bioinformatic step-by-step guide using quantitative structure-activity relationships.
    Hattotuwagama CK, Doytchinova IA, Flower DR.
    Methods Mol Biol; 2007 Jun 22; 409():227-45. PubMed ID: 18450004
    [Abstract] [Full Text] [Related]

  • 37. Ligand biological activity predictions using fingerprint-based artificial neural networks (FANN-QSAR).
    Myint KZ, Xie XQ.
    Methods Mol Biol; 2015 Jun 22; 1260():149-64. PubMed ID: 25502380
    [Abstract] [Full Text] [Related]

  • 38. Profile-QSAR: a novel meta-QSAR method that combines activities across the kinase family to accurately predict affinity, selectivity, and cellular activity.
    Martin E, Mukherjee P, Sullivan D, Jansen J.
    J Chem Inf Model; 2011 Aug 22; 51(8):1942-56. PubMed ID: 21667971
    [Abstract] [Full Text] [Related]

  • 39. A comparative QSAR study using CoMFA, HQSAR, and FRED/SKEYS paradigms for estrogen receptor binding affinities of structurally diverse compounds.
    Waller CL.
    J Chem Inf Comput Sci; 2004 Aug 22; 44(2):758-65. PubMed ID: 15032558
    [Abstract] [Full Text] [Related]

  • 40. A novel automated lazy learning QSAR (ALL-QSAR) approach: method development, applications, and virtual screening of chemical databases using validated ALL-QSAR models.
    Zhang S, Golbraikh A, Oloff S, Kohn H, Tropsha A.
    J Chem Inf Model; 2006 Aug 22; 46(5):1984-95. PubMed ID: 16995729
    [Abstract] [Full Text] [Related]

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