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Journal Abstract Search

126 related items for PubMed ID: 21426164

  • 41. Predicting kinase selectivity profiles using Free-Wilson QSAR analysis.
    Sciabola S, Stanton RV, Wittkopp S, Wildman S, Moshinsky D, Potluri S, Xi H.
    J Chem Inf Model; 2008 Sep; 48(9):1851-67. PubMed ID: 18717582
    [Abstract] [Full Text] [Related]

  • 42. Binding interaction analysis of the active site and its inhibitors for neuraminidase (N1 subtype) of human influenza virus by the integration of molecular docking, FMO calculation and 3D-QSAR CoMFA modeling.
    Zhang Q, Yang J, Liang K, Feng L, Li S, Wan J, Xu X, Yang G, Liu D, Yang S.
    J Chem Inf Model; 2008 Sep; 48(9):1802-12. PubMed ID: 18707092
    [Abstract] [Full Text] [Related]

  • 43. Identification of novel HCV RNA-dependent RNA polymerase inhibitors using pharmacophore-guided virtual screening.
    Kim J, Kim KS, Kim DE, Chong Y.
    Chem Biol Drug Des; 2008 Dec; 72(6):585-91. PubMed ID: 19090925
    [Abstract] [Full Text] [Related]

  • 44. 3D-QSAR studies on chromone derivatives as HIV-1 protease inhibitors: application of molecular field analysis.
    Nunthanavanit P, Anthony NG, Johnston BF, Mackay SP, Ungwitayatorn J.
    Arch Pharm (Weinheim); 2008 Jun; 341(6):357-64. PubMed ID: 18442018
    [Abstract] [Full Text] [Related]

  • 45. An integrated in silico analysis of drug-binding to human serum albumin.
    Estrada E, Uriarte E, Molina E, Simón-Manso Y, Milne GW.
    J Chem Inf Model; 2006 Jun; 46(6):2709-24. PubMed ID: 17125211
    [Abstract] [Full Text] [Related]

  • 46. 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
    [Abstract] [Full Text] [Related]

  • 47. Pharmacophore modeling, quantitative structure-activity relationship analysis, and shape-complemented in silico screening allow access to novel influenza neuraminidase inhibitors.
    Abu Hammad AM, Taha MO.
    J Chem Inf Model; 2009 Apr; 49(4):978-96. PubMed ID: 19341295
    [Abstract] [Full Text] [Related]

  • 48. Exploring QSARs for antiviral activity of 4-alkylamino-6-(2-hydroxyethyl)-2-methylthiopyrimidines by support vector machine.
    Riahi S, Pourbasheer E, Dinarvand R, Ganjali MR, Norouzi P.
    Chem Biol Drug Des; 2008 Sep; 72(3):205-16. PubMed ID: 18715229
    [Abstract] [Full Text] [Related]

  • 49. Structure-based rational quest for potential novel inhibitors of human HMG-CoA reductase by combining CoMFA 3D QSAR modeling and virtual screening.
    Zhang QY, Wan J, Xu X, Yang GF, Ren YL, Liu JJ, Wang H, Guo Y.
    J Comb Chem; 2007 Sep; 9(1):131-8. PubMed ID: 17206841
    [Abstract] [Full Text] [Related]

  • 50. The QSAR Paradigm in Fragment-Based Drug Discovery: From the Virtual Generation of Target Inhibitors to Multi-Scale Modeling.
    Kleandrova VV, Speck-Planche A.
    Mini Rev Med Chem; 2020 Sep; 20(14):1357-1374. PubMed ID: 32013845
    [Abstract] [Full Text] [Related]

  • 51. Recent advances in the development of antiviral agents using computer-aided structure based approaches.
    Kumar V, Chandra S, Siddiqi MI.
    Curr Pharm Des; 2014 Sep; 20(21):3488-99. PubMed ID: 24001227
    [Abstract] [Full Text] [Related]

  • 52. QSAR of phytochemicals for the design of better drugs.
    Kar S, Roy K.
    Expert Opin Drug Discov; 2012 Oct; 7(10):877-902. PubMed ID: 22897485
    [Abstract] [Full Text] [Related]

  • 53. Multitasking models for quantitative structure-biological effect relationships: current status and future perspectives to speed up drug discovery.
    Speck-Planche A, Cordeiro MN.
    Expert Opin Drug Discov; 2015 Mar; 10(3):245-56. PubMed ID: 25613725
    [Abstract] [Full Text] [Related]

  • 54. Advances in quantitative structure-activity relationship models of anti-Alzheimer's agents.
    Ambure P, Roy K.
    Expert Opin Drug Discov; 2014 Jun; 9(6):697-723. PubMed ID: 24754675
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  • 55. [Artificial ribonucleases: quantitative analysis of the structure-activity relationship and new insight into the strategy of design of highly efficient RNase mimetics].
    Koroleva LS, Kuz'min VE, Muratov EN, Artemenko AG, Sil'nikov VN.
    Bioorg Khim; 2008 Jun; 34(4):495-505. PubMed ID: 18695722
    [Abstract] [Full Text] [Related]

  • 56. Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR.
    Tropsha A, Isayev O, Varnek A, Schneider G, Cherkasov A.
    Nat Rev Drug Discov; 2024 Feb; 23(2):141-155. PubMed ID: 38066301
    [Abstract] [Full Text] [Related]

  • 57. A new adaptive L1-norm for optimal descriptor selection of high-dimensional QSAR classification model for anti-hepatitis C virus activity of thiourea derivatives.
    Algamal ZY, Lee MH.
    SAR QSAR Environ Res; 2017 Jan; 28(1):75-90. PubMed ID: 28176549
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  • 58. QSPR Prediction of Lipophilicity for Organic Compounds Using Random Forest Technique on the Basis of Simplex Representation of Molecular Structure.
    Ognichenko LN, Kuz'min VE, Gorb L, Hill FC, Artemenko AG, Polischuk PG, Leszczynski J.
    Mol Inform; 2012 Apr; 31(3-4):273-80. PubMed ID: 27477097
    [Abstract] [Full Text] [Related]

  • 59. Structural and Physico-Chemical Interpretation (SPCI) of QSAR Models and Its Comparison with Matched Molecular Pair Analysis.
    Polishchuk P, Tinkov O, Khristova T, Ognichenko L, Kosinskaya A, Varnek A, Kuz'min V.
    J Chem Inf Model; 2016 Aug 22; 56(8):1455-69. PubMed ID: 27419846
    [Abstract] [Full Text] [Related]

  • 60. Double focus in the modelling of anti-influenza properties of 2-iminobenzimidazolines: pharmacology and toxicology.
    Pereira IV, de Freitas MP.
    SAR QSAR Environ Res; 2021 Aug 22; 32(8):643-654. PubMed ID: 34282674
    [Abstract] [Full Text] [Related]

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