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Journal Abstract Search

102 related items for PubMed ID: 16275084

  • 1. New tyrosinase inhibitors selected by atomic linear indices-based classification models.
    Casañola-Martín GM, Khan MT, Marrero-Ponce Y, Ather A, Sultankhodzhaev MN, Torrens F.
    Bioorg Med Chem Lett; 2006 Jan 15; 16(2):324-30. PubMed ID: 16275084
    [Abstract] [Full Text] [Related]

  • 2. Prediction of tyrosinase inhibition activity using atom-based bilinear indices.
    Marrero-Ponce Y, Khan MT, Casañola Martín GM, Ather A, Sultankhodzhaev MN, Torrens F, Rotondo R.
    ChemMedChem; 2007 Apr 15; 2(4):449-78. PubMed ID: 17366651
    [Abstract] [Full Text] [Related]

  • 3. TOMOCOMD-CARDD descriptors-based virtual screening of tyrosinase inhibitors: evaluation of different classification model combinations using bond-based linear indices.
    Casañola-Martín GM, Marrero-Ponce Y, Khan MT, Ather A, Sultan S, Torrens F, Rotondo R.
    Bioorg Med Chem; 2007 Feb 01; 15(3):1483-503. PubMed ID: 17110117
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  • 5. Bond-based 2D quadratic fingerprints in QSAR studies: virtual and in vitro tyrosinase inhibitory activity elucidation.
    Casañola-Martin GM, Marrero-Ponce Y, Khan MT, Khan SB, Torrens F, Pérez-Jiménez F, Rescigno A, Abad C.
    Chem Biol Drug Des; 2010 Dec 01; 76(6):538-45. PubMed ID: 20964806
    [Abstract] [Full Text] [Related]

  • 6. Dragon method for finding novel tyrosinase inhibitors: Biosilico identification and experimental in vitro assays.
    Casañola-Martín GM, Marrero-Ponce Y, Khan MT, Ather A, Khan KM, Torrens F, Rotondo R.
    Eur J Med Chem; 2007 Dec 01; 42(11-12):1370-81. PubMed ID: 17637486
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  • 8. Bond-based 2D TOMOCOMD-CARDD approach for drug discovery: aiding decision-making in 'in silico' selection of new lead tyrosinase inhibitors.
    Marrero-Ponce Y, Khan MT, Casañola-Martín GM, Ather A, Sultankhodzhaev MN, García-Domenech R, Torrens F, Rotondo R.
    J Comput Aided Mol Des; 2007 Apr 01; 21(4):167-88. PubMed ID: 17333484
    [Abstract] [Full Text] [Related]

  • 9. Atom, atom-type, and total nonstochastic and stochastic quadratic fingerprints: a promising approach for modeling of antibacterial activity.
    Marrero-Ponce Y, Medina-Marrero R, Torrens F, Martinez Y, Romero-Zaldivar V, Castro EA.
    Bioorg Med Chem; 2005 Apr 15; 13(8):2881-99. PubMed ID: 15781398
    [Abstract] [Full Text] [Related]

  • 10. Atom- and bond-based 2D TOMOCOMD-CARDD approach and ligand-based virtual screening for the drug discovery of new tyrosinase inhibitors.
    Casañola-Martín GM, Marrero-Ponce Y, Tareq Hassan Khan M, Torrens F, Pérez-Giménez F, Rescigno A.
    J Biomol Screen; 2008 Dec 15; 13(10):1014-24. PubMed ID: 19015291
    [Abstract] [Full Text] [Related]

  • 11. Kojic acid-amino acid conjugates as tyrosinase inhibitors.
    Noh JM, Kwak SY, Seo HS, Seo JH, Kim BG, Lee YS.
    Bioorg Med Chem Lett; 2009 Oct 01; 19(19):5586-9. PubMed ID: 19700313
    [Abstract] [Full Text] [Related]

  • 12. Non-stochastic quadratic fingerprints and LDA-based QSAR models in hit and lead generation through virtual screening: theoretical and experimental assessment of a promising method for the discovery of new antimalarial compounds.
    Montero-Torres A, García-Sánchez RN, Marrero-Ponce Y, Machado-Tugores Y, Nogal-Ruiz JJ, Martínez-Fernández AR, Arán VJ, Ochoa C, Meneses-Marcel A, Torrens F.
    Eur J Med Chem; 2006 Apr 01; 41(4):483-93. PubMed ID: 16545891
    [Abstract] [Full Text] [Related]

  • 13. Tyrosinase inhibitory cycloartane type triterpenoids from the methanol extract of the whole plant of Amberboa ramosa Jafri and their structure-activity relationship.
    Khan MT, Khan SB, Ather A.
    Bioorg Med Chem; 2006 Feb 15; 14(4):938-43. PubMed ID: 16202612
    [Abstract] [Full Text] [Related]

  • 14. 3D-QSAR with the aid of pharmacophore search and docking-based alignments for farnesyltransferase inhibitors.
    Vaidya M, Weigt M, Wiese M.
    Eur J Med Chem; 2009 Oct 15; 44(10):4070-82. PubMed ID: 19515462
    [Abstract] [Full Text] [Related]

  • 15. 3D-QSAR and molecular docking studies of benzaldehyde thiosemicarbazone, benzaldehyde, benzoic acid, and their derivatives as phenoloxidase inhibitors.
    Xue CB, Zhang L, Luo WC, Xie XY, Jiang L, Xiao T.
    Bioorg Med Chem; 2007 Mar 01; 15(5):2006-15. PubMed ID: 17258462
    [Abstract] [Full Text] [Related]

  • 16. Discovery of small-molecule inhibitors of tyrosinase.
    Germanas JP, Wang S, Miner A, Hao W, Ready JM.
    Bioorg Med Chem Lett; 2007 Dec 15; 17(24):6871-5. PubMed ID: 17964155
    [Abstract] [Full Text] [Related]

  • 17. Non-stochastic and stochastic linear indices of the 'molecular pseudograph's atom adjacency matrix': application to 'in silico' studies for the rational discovery of new antimalarial compounds.
    Marrero-Ponce Y, Montero-Torres A, Zaldivar CR, Veitía MI, Peréz MM, Sánchez RN.
    Bioorg Med Chem; 2005 Feb 15; 13(4):1293-304. PubMed ID: 15670938
    [Abstract] [Full Text] [Related]

  • 18. Tyrosinase inhibitors from Artocarpus gomezianus.
    Likhitwitayawuid K, Sritularak B, De-Eknamkul W.
    Planta Med; 2000 Apr 15; 66(3):275-7. PubMed ID: 10821057
    [Abstract] [Full Text] [Related]

  • 19. Atom-based 3D-chiral quadratic indices. Part 2: prediction of the corticosteroid-binding globulinbinding affinity of the 31 benchmark steroids data set.
    Castillo-Garit JA, Marrero-Ponce Y, Torrens F.
    Bioorg Med Chem; 2006 Apr 01; 14(7):2398-408. PubMed ID: 16325409
    [Abstract] [Full Text] [Related]

  • 20. An efficient tool for identifying inhibitors based on 3D-QSAR and docking using feature-shape pharmacophore of biologically active conformation--a case study with CDK2/cyclinA.
    Mascarenhas NM, Ghoshal N.
    Eur J Med Chem; 2008 Dec 01; 43(12):2807-18. PubMed ID: 18037537
    [Abstract] [Full Text] [Related]

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