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143 related items for PubMed ID: 25361075

  • 1. Experimental and computational prediction of glass transition temperature of drugs.
    Alzghoul A, Alhalaweh A, Mahlin D, Bergström CA.
    J Chem Inf Model; 2014 Dec 22; 54(12):3396-403. PubMed ID: 25361075
    [Abstract] [Full Text] [Related]

  • 2. QSPR correlation of melting point for drug compounds based on different sources of molecular descriptors.
    Modarresi H, Dearden JC, Modarress H.
    J Chem Inf Model; 2006 Dec 22; 46(2):930-6. PubMed ID: 16563024
    [Abstract] [Full Text] [Related]

  • 3. Toward in silico prediction of glass-forming ability from molecular structure alone: a screening tool in early drug development.
    Mahlin D, Ponnambalam S, Höckerfelt MH, Bergström CA.
    Mol Pharm; 2011 Apr 04; 8(2):498-506. PubMed ID: 21344945
    [Abstract] [Full Text] [Related]

  • 4. Physical stability of drugs after storage above and below the glass transition temperature: Relationship to glass-forming ability.
    Alhalaweh A, Alzghoul A, Mahlin D, Bergström CAS.
    Int J Pharm; 2015 Nov 10; 495(1):312-317. PubMed ID: 26341321
    [Abstract] [Full Text] [Related]

  • 5. Prediction of retention indices of drugs based on immobilized artificial membrane chromatography using Projection Pursuit Regression and Local Lazy Regression.
    Du H, Watzl J, Wang J, Zhang X, Yao X, Hu Z.
    J Sep Sci; 2008 Jul 10; 31(12):2325-33. PubMed ID: 18491354
    [Abstract] [Full Text] [Related]

  • 6. Exhaustive QSPR studies of a large diverse set of ionic liquids: how accurately can we predict melting points?
    Varnek A, Kireeva N, Tetko IV, Baskin II, Solov'ev VP.
    J Chem Inf Model; 2007 Jul 10; 47(3):1111-22. PubMed ID: 17381081
    [Abstract] [Full Text] [Related]

  • 7. Early drug development predictions of glass-forming ability and physical stability of drugs.
    Mahlin D, Bergström CA.
    Eur J Pharm Sci; 2013 May 13; 49(2):323-32. PubMed ID: 23557841
    [Abstract] [Full Text] [Related]

  • 8. The peculiar behavior of the glass transition temperature of amorphous drug-polymer films coated on inert sugar spheres.
    Dereymaker A, Van Den Mooter G.
    J Pharm Sci; 2015 May 13; 104(5):1759-66. PubMed ID: 25702912
    [Abstract] [Full Text] [Related]

  • 9. Computational predictions of glass-forming ability and crystallization tendency of drug molecules.
    Alhalaweh A, Alzghoul A, Kaialy W, Mahlin D, Bergström CA.
    Mol Pharm; 2014 Sep 02; 11(9):3123-32. PubMed ID: 25014125
    [Abstract] [Full Text] [Related]

  • 10. 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 02; 74(2):142-7. PubMed ID: 19549084
    [Abstract] [Full Text] [Related]

  • 11. Molecular descriptors influencing melting point and their role in classification of solid drugs.
    Bergström CA, Norinder U, Luthman K, Artursson P.
    J Chem Inf Comput Sci; 2003 Aug 02; 43(4):1177-85. PubMed ID: 12870909
    [Abstract] [Full Text] [Related]

  • 12. Predicting myelosuppression of drugs from in silico models.
    Crivori P, Pennella G, Magistrelli M, Grossi P, Giusti AM.
    J Chem Inf Model; 2011 Feb 28; 51(2):434-45. PubMed ID: 21275392
    [Abstract] [Full Text] [Related]

  • 13. In-silico prediction of blood-brain barrier permeability.
    Yan A, Liang H, Chong Y, Nie X, Yu C.
    SAR QSAR Environ Res; 2013 Jan 28; 24(1):61-74. PubMed ID: 23092117
    [Abstract] [Full Text] [Related]

  • 14. A molecular dynamics approach for predicting the glass transition temperature and plasticization effect in amorphous pharmaceuticals.
    Gupta J, Nunes C, Jonnalagadda S.
    Mol Pharm; 2013 Nov 04; 10(11):4136-45. PubMed ID: 24074140
    [Abstract] [Full Text] [Related]

  • 15. 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 04; 48(1):220-32. PubMed ID: 18186622
    [Abstract] [Full Text] [Related]

  • 16. In silico prediction of volume of distribution in human using linear and nonlinear models on a 669 compound data set.
    Berellini G, Springer C, Waters NJ, Lombardo F.
    J Med Chem; 2009 Jul 23; 52(14):4488-95. PubMed ID: 19603833
    [Abstract] [Full Text] [Related]

  • 17. Prediction of drug intestinal absorption by new linear and non-linear QSPR.
    Talevi A, Goodarzi M, Ortiz EV, Duchowicz PR, Bellera CL, Pesce G, Castro EA, Bruno-Blanch LE.
    Eur J Med Chem; 2011 Jan 23; 46(1):218-28. PubMed ID: 21112128
    [Abstract] [Full Text] [Related]

  • 18. In silico prediction of the solubility advantage for amorphous drugs - Are there property-based rules for drug discovery and early pharmaceutical development?
    Kuentz M, Imanidis G.
    Eur J Pharm Sci; 2013 Feb 14; 48(3):554-62. PubMed ID: 23262058
    [Abstract] [Full Text] [Related]

  • 19. Benchmarking of linear and nonlinear approaches for quantitative structure-property relationship studies of metal complexation with ionophores.
    Tetko IV, Solov'ev VP, Antonov AV, Yao X, Doucet JP, Fan B, Hoonakker F, Fourches D, Jost P, Lachiche N, Varnek A.
    J Chem Inf Model; 2006 Feb 14; 46(2):808-19. PubMed ID: 16563012
    [Abstract] [Full Text] [Related]

  • 20. How accurately can we predict the melting points of drug-like compounds?
    Tetko IV, Sushko Y, Novotarskyi S, Patiny L, Kondratov I, Petrenko AE, Charochkina L, Asiri AM.
    J Chem Inf Model; 2014 Dec 22; 54(12):3320-9. PubMed ID: 25489863
    [Abstract] [Full Text] [Related]

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