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Journal Abstract Search

324 related items for PubMed ID: 19054595

  • 1. QSAR models for 2-amino-6-arylsulfonylbenzonitriles and congeners HIV-1 reverse transcriptase inhibitors based on linear and nonlinear regression methods.
    Hu R, Doucet JP, Delamar M, Zhang R.
    Eur J Med Chem; 2009 May; 44(5):2158-71. PubMed ID: 19054595
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  • 2. QSAR modeling of HIV-1 reverse transcriptase inhibitor 2-amino-6-arylsulfonylbenzonitriles and congeners using molecular connectivity and E-state parameters.
    Roy K, Leonard JT.
    Bioorg Med Chem; 2004 Feb 15; 12(4):745-54. PubMed ID: 14759734
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  • 3. MIA-QSAR modelling of anti-HIV-1 activities of some 2-amino-6-arylsulfonylbenzonitriles and their thio and sulfinyl congeners.
    Freitas MP.
    Org Biomol Chem; 2006 Mar 21; 4(6):1154-9. PubMed ID: 16525561
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  • 4. Comparative study of QSAR/QSPR correlations using support vector machines, radial basis function neural networks, and multiple linear regression.
    Yao XJ, Panaye A, Doucet JP, Zhang RS, Chen HF, Liu MC, Hu ZD, Fan BT.
    J Chem Inf Comput Sci; 2004 Mar 21; 44(4):1257-66. PubMed ID: 15272833
    [Abstract] [Full Text] [Related]

  • 5. Quantitative structure-activity relationship studies on 2-amino-6-arylsulfonylbenzonitriles as human immunodeficiency viruses type 1 reverse transcriptase inhibitors using descriptors obtained from substituents and whole molecular structures.
    Hemmateenejad B, Sabet R, Fassihi A.
    Chem Biol Drug Des; 2009 Oct 21; 74(4):405-15. PubMed ID: 19691465
    [Abstract] [Full Text] [Related]

  • 6. Predictive QSAR modeling of HIV reverse transcriptase inhibitor TIBO derivatives.
    Mandal AS, Roy K.
    Eur J Med Chem; 2009 Apr 21; 44(4):1509-24. PubMed ID: 18760864
    [Abstract] [Full Text] [Related]

  • 7. Quantitative predictions of gas chromatography retention indexes with support vector machines, radial basis neural networks and multiple linear regression.
    Chen HF.
    Anal Chim Acta; 2008 Feb 18; 609(1):24-36. PubMed ID: 18243870
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  • 8. Application of support vector machine (SVM) for prediction toxic activity of different data sets.
    Zhao CY, Zhang HX, Zhang XY, Liu MC, Hu ZD, Fan BT.
    Toxicology; 2006 Jan 16; 217(2-3):105-19. PubMed ID: 16213080
    [Abstract] [Full Text] [Related]

  • 9. Prediction of CCR5 receptor binding affinity of substituted 1-(3,3-diphenylpropyl)-piperidinyl amides and ureas based on the heuristic method, support vector machine and projection pursuit regression.
    Yuan Y, Zhang R, Hu R, Ruan X.
    Eur J Med Chem; 2009 Jan 16; 44(1):25-34. PubMed ID: 18433938
    [Abstract] [Full Text] [Related]

  • 10. QSAR study of PETT derivatives as potent HIV-1 reverse transcriptase inhibitors.
    Sabet R, Fassihi A, Moeinifard B.
    J Mol Graph Model; 2009 Sep 16; 28(2):146-55. PubMed ID: 19570701
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  • 14. Quantitative structure-activity relationship models for prediction of sensory irritants (logRD50) of volatile organic chemicals.
    Luan F, Ma W, Zhang X, Zhang H, Liu M, Hu Z, Fan BT.
    Chemosphere; 2006 May 16; 63(7):1142-53. PubMed ID: 16307788
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  • 18. Application of PC-ANN and PC-LS-SVM in QSAR of CCR1 antagonist compounds: a comparative study.
    Shahlaei M, Fassihi A, Saghaie L.
    Eur J Med Chem; 2010 Apr 16; 45(4):1572-82. PubMed ID: 20170987
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