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Journal Abstract Search

209 related items for PubMed ID: 16045282

  • 1. "In-house likeness": comparison of large compound collections using artificial neural networks.
    Muresan S, Sadowski J.
    J Chem Inf Model; 2005; 45(4):888-93. PubMed ID: 16045282
    [Abstract] [Full Text] [Related]

  • 2. Development of a method for evaluating drug-likeness and ease of synthesis using a data set in which compounds are assigned scores based on chemists' intuition.
    Takaoka Y, Endo Y, Yamanobe S, Kakinuma H, Okubo T, Shimazaki Y, Ota T, Sumiya S, Yoshikawa K.
    J Chem Inf Comput Sci; 2003; 43(4):1269-75. PubMed ID: 12870920
    [Abstract] [Full Text] [Related]

  • 3. Applications of self-organizing neural networks in virtual screening and diversity selection.
    Selzer P, Ertl P.
    J Chem Inf Model; 2006; 46(6):2319-23. PubMed ID: 17125175
    [Abstract] [Full Text] [Related]

  • 4. Drug discovery using support vector machines. The case studies of drug-likeness, agrochemical-likeness, and enzyme inhibition predictions.
    Zernov VV, Balakin KV, Ivaschenko AA, Savchuk NP, Pletnev IV.
    J Chem Inf Comput Sci; 2003; 43(6):2048-56. PubMed ID: 14632457
    [Abstract] [Full Text] [Related]

  • 5. A scoring scheme for discriminating between drugs and nondrugs.
    Sadowski J, Kubinyi H.
    J Med Chem; 1998 Aug 27; 41(18):3325-9. PubMed ID: 9719584
    [Abstract] [Full Text] [Related]

  • 6. In silico log P prediction for a large data set with support vector machines, radial basis neural networks and multiple linear regression.
    Chen HF.
    Chem Biol Drug Des; 2009 Aug 27; 74(2):142-7. PubMed ID: 19549084
    [Abstract] [Full Text] [Related]

  • 7. Synthetic library design.
    Huwe CM.
    Drug Discov Today; 2006 Aug 27; 11(15-16):763-7. PubMed ID: 16846805
    [Abstract] [Full Text] [Related]

  • 8. Classifying "kinase inhibitor-likeness" by using machine-learning methods.
    Briem H, Günther J.
    Chembiochem; 2005 Mar 27; 6(3):558-66. PubMed ID: 15696507
    [Abstract] [Full Text] [Related]

  • 9. Data structures and computational tools for the extraction of SAR information from large compound sets.
    Wawer M, Lounkine E, Wassermann AM, Bajorath J.
    Drug Discov Today; 2010 Aug 27; 15(15-16):630-9. PubMed ID: 20547243
    [Abstract] [Full Text] [Related]

  • 10. Emerging chemical patterns: a new methodology for molecular classification and compound selection.
    Auer J, Bajorath J.
    J Chem Inf Model; 2006 Aug 27; 46(6):2502-14. PubMed ID: 17125191
    [Abstract] [Full Text] [Related]

  • 11. Prediction of enantiomeric excess in a combinatorial library of catalytic enantioselective reactions.
    Aires-de-Sousa J, Gasteiger J.
    J Comb Chem; 2005 Aug 27; 7(2):298-301. PubMed ID: 15762759
    [Abstract] [Full Text] [Related]

  • 12. Molecular shape diversity of combinatorial libraries: a prerequisite for broad bioactivity.
    Sauer WH, Schwarz MK.
    J Chem Inf Comput Sci; 2003 Aug 27; 43(3):987-1003. PubMed ID: 12767158
    [Abstract] [Full Text] [Related]

  • 13. Selective optimization of side activities: another way for drug discovery.
    Wermuth CG.
    J Med Chem; 2004 Mar 11; 47(6):1303-14. PubMed ID: 14998318
    [No Abstract] [Full Text] [Related]

  • 14. CLEVER: pipeline for designing in silico chemical libraries.
    Song CM, Bernardo PH, Chai CL, Tong JC.
    J Mol Graph Model; 2009 Jan 11; 27(5):578-83. PubMed ID: 18986817
    [Abstract] [Full Text] [Related]

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  • 19. A sorcerer's apprentice and The Rule of Five: from rule-of-thumb to commandment and beyond.
    Abad-Zapatero C.
    Drug Discov Today; 2007 Dec 11; 12(23-24):995-7. PubMed ID: 18061876
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