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Journal Abstract Search

950 related items for PubMed ID: 23363443

  • 1. Integration of in silico and in vitro tools for scaffold optimization during drug discovery: predicting P-glycoprotein efflux.
    Desai PV, Sawada GA, Watson IA, Raub TJ.
    Mol Pharm; 2013 Apr 01; 10(4):1249-61. PubMed ID: 23363443
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  • 2. ADMET evaluation in drug discovery. 13. Development of in silico prediction models for P-glycoprotein substrates.
    Li D, Chen L, Li Y, Tian S, Sun H, Hou T.
    Mol Pharm; 2014 Mar 03; 11(3):716-26. PubMed ID: 24499501
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  • 4. Integrating in Silico and in Vitro Approaches To Predict Drug Accessibility to the Central Nervous System.
    Zhang YY, Liu H, Summerfield SG, Luscombe CN, Sahi J.
    Mol Pharm; 2016 May 02; 13(5):1540-50. PubMed ID: 27015243
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  • 13. 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
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  • 14. Classification of substrates and inhibitors of P-glycoprotein using unsupervised machine learning approach.
    Wang YH, Li Y, Yang SL, Yang L.
    J Chem Inf Model; 2005 Aug 22; 45(3):750-7. PubMed ID: 15921464
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  • 15. ADME evaluation in drug discovery. 5. Correlation of Caco-2 permeation with simple molecular properties.
    Hou TJ, Zhang W, Xia K, Qiao XB, Xu XJ.
    J Chem Inf Comput Sci; 2004 Aug 22; 44(5):1585-600. PubMed ID: 15446816
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  • 19. Computational models for predicting substrates or inhibitors of P-glycoprotein.
    Chen L, Li Y, Yu H, Zhang L, Hou T.
    Drug Discov Today; 2012 Apr 22; 17(7-8):343-51. PubMed ID: 22119877
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