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Journal Abstract Search

166 related items for PubMed ID: 29953260

  • 1. Confident application of a global human liver microsomal activity QSAR.
    Stålring J, Sohlenius-Sternbeck AK, Terelius Y, Parkes K.
    Future Med Chem; 2018 Jul 01; 10(13):1575-1588. PubMed ID: 29953260
    [Abstract] [Full Text] [Related]

  • 2. Critically Assessing the Predictive Power of QSAR Models for Human Liver Microsomal Stability.
    Liu R, Schyman P, Wallqvist A.
    J Chem Inf Model; 2015 Aug 24; 55(8):1566-75. PubMed ID: 26170251
    [Abstract] [Full Text] [Related]

  • 3. Development of QSAR models for microsomal stability: identification of good and bad structural features for rat, human and mouse microsomal stability.
    Hu Y, Unwalla R, Denny RA, Bikker J, Di L, Humblet C.
    J Comput Aided Mol Des; 2010 Jan 24; 24(1):23-35. PubMed ID: 19937264
    [Abstract] [Full Text] [Related]

  • 4. Development of in silico models for human liver microsomal stability.
    Lee PH, Cucurull-Sanchez L, Lu J, Du YJ.
    J Comput Aided Mol Des; 2007 Dec 24; 21(12):665-73. PubMed ID: 17599241
    [Abstract] [Full Text] [Related]

  • 5. A probabilistic method to report predictions from a human liver microsomes stability QSAR model: a practical tool for drug discovery.
    Aliagas I, Gobbi A, Heffron T, Lee ML, Ortwine DF, Zak M, Khojasteh SC.
    J Comput Aided Mol Des; 2015 Apr 24; 29(4):327-38. PubMed ID: 25708388
    [Abstract] [Full Text] [Related]

  • 6. Predicting Mouse Liver Microsomal Stability with "Pruned" Machine Learning Models and Public Data.
    Perryman AL, Stratton TP, Ekins S, Freundlich JS.
    Pharm Res; 2016 Feb 24; 33(2):433-49. PubMed ID: 26415647
    [Abstract] [Full Text] [Related]

  • 7. The current limits in virtual screening and property prediction.
    Hutter MC.
    Future Med Chem; 2018 Jul 01; 10(13):1623-1635. PubMed ID: 29953247
    [Abstract] [Full Text] [Related]

  • 8. Predictive QSAR modeling workflow, model applicability domains, and virtual screening.
    Tropsha A, Golbraikh A.
    Curr Pharm Des; 2007 Jul 01; 13(34):3494-504. PubMed ID: 18220786
    [Abstract] [Full Text] [Related]

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  • 10. Prediction of hepatic microsomal intrinsic clearance and human clearance values for drugs.
    Nikolic K, Agababa D.
    J Mol Graph Model; 2009 Oct 01; 28(3):245-52. PubMed ID: 19713138
    [Abstract] [Full Text] [Related]

  • 11. Cellular target engagement: a new paradigm in drug discovery.
    Babic I, Kesari S, Nurmemmedov E.
    Future Med Chem; 2018 Jul 01; 10(14):1641-1644. PubMed ID: 29957028
    [No Abstract] [Full Text] [Related]

  • 12. Comparative Proteomics Analysis of Human Liver Microsomes and S9 Fractions.
    Wang X, He B, Shi J, Li Q, Zhu HJ.
    Drug Metab Dispos; 2020 Jan 01; 48(1):31-40. PubMed ID: 31699809
    [Abstract] [Full Text] [Related]

  • 13. Prediction of Fraction Unbound in Microsomal and Hepatocyte Incubations: A Comparison of Methods across Industry Datasets.
    Winiwarter S, Chang G, Desai P, Menzel K, Faller B, Arimoto R, Keefer C, Broccatell F.
    Mol Pharm; 2019 Sep 03; 16(9):4077-4085. PubMed ID: 31348668
    [Abstract] [Full Text] [Related]

  • 14. A Fragment-Based Approach for the Computational Prediction of the Nonspecific Binding of Drugs to Hepatic Microsomes.
    Nair PC, McKinnon RA, Miners JO.
    Drug Metab Dispos; 2016 Nov 03; 44(11):1794-1798. PubMed ID: 27543205
    [Abstract] [Full Text] [Related]

  • 15. Assessment of in silico models for fraction of unbound drug in human liver microsomes.
    Gao H, Steyn SJ, Chang G, Lin J.
    Expert Opin Drug Metab Toxicol; 2010 May 03; 6(5):533-42. PubMed ID: 20233033
    [Abstract] [Full Text] [Related]

  • 16. Using open source computational tools for predicting human metabolic stability and additional absorption, distribution, metabolism, excretion, and toxicity properties.
    Gupta RR, Gifford EM, Liston T, Waller CL, Hohman M, Bunin BA, Ekins S.
    Drug Metab Dispos; 2010 Nov 03; 38(11):2083-90. PubMed ID: 20693417
    [Abstract] [Full Text] [Related]

  • 17. Prediction of Human Liver Microsome Clearance with Chirality-Focused Graph Neural Networks.
    Pu C, Gu L, Hu Y, Han W, Xu X, Liu H, Chen Y, Zhang Y.
    J Chem Inf Model; 2024 Jul 22; 64(14):5427-5438. PubMed ID: 38976447
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  • 20. Quantitative structure-activity relationship models for predicting drug-induced liver injury based on FDA-approved drug labeling annotation and using a large collection of drugs.
    Chen M, Hong H, Fang H, Kelly R, Zhou G, Borlak J, Tong W.
    Toxicol Sci; 2013 Nov 22; 136(1):242-9. PubMed ID: 23997115
    [Abstract] [Full Text] [Related]

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