These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

93 related articles for article (PubMed ID: 10346926)

  • 1. Molecular hashkeys: a novel method for molecular characterization and its application for predicting important pharmaceutical properties of molecules.
    Ghuloum AM; Sage CR; Jain AN
    J Med Chem; 1999 May; 42(10):1739-48. PubMed ID: 10346926
    [TBL] [Abstract][Full Text] [Related]  

  • 2. ADME evaluation in drug discovery. 2. Prediction of partition coefficient by atom-additive approach based on atom-weighted solvent accessible surface areas.
    Hou TJ; Xu XJ
    J Chem Inf Comput Sci; 2003; 43(3):1058-67. PubMed ID: 12767165
    [TBL] [Abstract][Full Text] [Related]  

  • 3. ADME evaluation in drug discovery. 8. The prediction of human intestinal absorption by a support vector machine.
    Hou T; Wang J; Li Y
    J Chem Inf Model; 2007; 47(6):2408-15. PubMed ID: 17929911
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Use of surface charges from DFT calculations to predict intestinal absorption.
    Jones R; Connolly PC; Klamt A; Diedenhofen M
    J Chem Inf Model; 2005; 45(5):1337-42. PubMed ID: 16180910
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A fully computational model for predicting percutaneous drug absorption.
    Neumann D; Kohlbacher O; Merkwirth C; Lengauer T
    J Chem Inf Model; 2006; 46(1):424-9. PubMed ID: 16426076
    [TBL] [Abstract][Full Text] [Related]  

  • 6. [Strategy of molecular design of drugs: the unification of macro-properties and micro-structures of a molecule].
    Guo ZR
    Yao Xue Xue Bao; 2008 Mar; 43(3):227-33. PubMed ID: 18630256
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Artificial neural networks analysis used to evaluate the molecular interactions between selected drugs and human alpha1-acid glycoprotein.
    Buciński A; Wnuk M; Goryński K; Giza A; Kochańczyk J; Nowaczyk A; Baczek T; Nasal A
    J Pharm Biomed Anal; 2009 Nov; 50(4):591-6. PubMed ID: 19117712
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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; 74(2):142-7. PubMed ID: 19549084
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Predictive model of blood-brain barrier penetration of organic compounds.
    Ma XL; Chen C; Yang J
    Acta Pharmacol Sin; 2005 Apr; 26(4):500-12. PubMed ID: 15780201
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Using general regression and probabilistic neural networks to predict human intestinal absorption with topological descriptors derived from two-dimensional chemical structures.
    Niwa T
    J Chem Inf Comput Sci; 2003; 43(1):113-9. PubMed ID: 12546543
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Quantitative structure-property relationship study of n-octanol-water partition coefficients of some of diverse drugs using multiple linear regression.
    Ghasemi J; Saaidpour S
    Anal Chim Acta; 2007 Dec; 604(2):99-106. PubMed ID: 17996529
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Similarity metrics for ligands reflecting the similarity of the target proteins.
    Schuffenhauer A; Floersheim P; Acklin P; Jacoby E
    J Chem Inf Comput Sci; 2003; 43(2):391-405. PubMed ID: 12653501
    [TBL] [Abstract][Full Text] [Related]  

  • 13. On-the-fly selection of a training set for aqueous solubility prediction.
    Zhang H; Ando HY; Chen L; Lee PH
    Mol Pharm; 2007; 4(4):489-97. PubMed ID: 17628076
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Why are some properties more difficult to predict than others? A study of QSPR models of solubility, melting point, and Log P.
    Hughes LD; Palmer DS; Nigsch F; Mitchell JB
    J Chem Inf Model; 2008 Jan; 48(1):220-32. PubMed ID: 18186622
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Generalized fragment-substructure based property prediction method.
    Clark M
    J Chem Inf Model; 2005; 45(1):30-8. PubMed ID: 15667126
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Development of reliable aqueous solubility models and their application in druglike analysis.
    Wang J; Krudy G; Hou T; Zhang W; Holland G; Xu X
    J Chem Inf Model; 2007; 47(4):1395-404. PubMed ID: 17569522
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Molecule kernels: a descriptor- and alignment-free quantitative structure-activity relationship approach.
    Mohr JA; Jain BJ; Obermayer K
    J Chem Inf Model; 2008 Sep; 48(9):1868-81. PubMed ID: 18767832
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Prediction of aqueous solubility of a diverse set of compounds using quantitative structure-property relationships.
    Cheng A; Merz KM
    J Med Chem; 2003 Aug; 46(17):3572-80. PubMed ID: 12904062
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Application of ALOGPS 2.1 to predict log D distribution coefficient for Pfizer proprietary compounds.
    Tetko IV; Poda GI
    J Med Chem; 2004 Nov; 47(23):5601-4. PubMed ID: 15509156
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Machine learning models for lipophilicity and their domain of applicability.
    Schroeter T; Schwaighofer A; Mika S; Laak AT; Suelzle D; Ganzer U; Heinrich N; Müller KR
    Mol Pharm; 2007; 4(4):524-38. PubMed ID: 17637064
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 5.