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Journal Abstract Search

182 related items for PubMed ID: 27463195

  • 21. Scores of extended connectivity fingerprint as descriptors in QSPR study of melting point and aqueous solubility.
    Zhou D, Alelyunas Y, Liu R.
    J Chem Inf Model; 2008 May; 48(5):981-7. PubMed ID: 18465850
    [Abstract] [Full Text] [Related]

  • 22. Prediction of aqueous solubility of organic compounds using a quantitative structure-property relationship.
    Chen XQ, Cho SJ, Li Y, Venkatesh S.
    J Pharm Sci; 2002 Aug; 91(8):1838-52. PubMed ID: 12115811
    [Abstract] [Full Text] [Related]

  • 23. QSPR modelling of dielectric constants of π-conjugated organic compounds by means of the CORAL software.
    Achary PG.
    SAR QSAR Environ Res; 2014 Aug; 25(6):507-26. PubMed ID: 24716837
    [Abstract] [Full Text] [Related]

  • 24. Estimation of aqueous solubility of organic compounds with QSPR approach.
    Gao H, Shanmugasundaram V, Lee P.
    Pharm Res; 2002 Apr; 19(4):497-503. PubMed ID: 12033386
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  • 25. Linear and nonlinear quantitative structure-property relationship models for solubility of some anthraquinone, anthrone and xanthone derivatives in supercritical carbon dioxide.
    Hemmateenejad B, Shamsipur M, Miri R, Elyasi M, Foroghinia F, Sharghi H.
    Anal Chim Acta; 2008 Mar 03; 610(1):25-34. PubMed ID: 18267136
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  • 26. Comparison of Multiple Linear Regressions and Neural Networks based QSAR models for the design of new antitubercular compounds.
    Ventura C, Latino DA, Martins F.
    Eur J Med Chem; 2013 Mar 03; 70():831-45. PubMed ID: 24246731
    [Abstract] [Full Text] [Related]

  • 27. Prediction of the maximum absorption wavelength of azobenzene dyes by QSPR tools.
    Xu X, Luan F, Liu H, Cheng J, Zhang X.
    Spectrochim Acta A Mol Biomol Spectrosc; 2011 Dec 03; 83(1):353-61. PubMed ID: 21930420
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  • 28. Prediction of impact sensitivity of nitro energetic compounds by neural network based on electrotopological-state indices.
    Wang R, Jiang J, Pan Y, Cao H, Cui Y.
    J Hazard Mater; 2009 Jul 15; 166(1):155-86. PubMed ID: 19101083
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  • 29. Prediction of the Auto-Ignition Temperatures of Binary Miscible Liquid Mixtures from Molecular Structures.
    Shen S, Pan Y, Ji X, Ni Y, Jiang J.
    Int J Mol Sci; 2019 Apr 27; 20(9):. PubMed ID: 31035591
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  • 30. Linear and nonlinear functions on modeling of aqueous solubility of organic compounds by two structure representation methods.
    Yan A, Gasteiger J, Krug M, Anzali S.
    J Comput Aided Mol Des; 2004 Feb 27; 18(2):75-87. PubMed ID: 15287695
    [Abstract] [Full Text] [Related]

  • 31. Rank-based ant system method for non-linear QSPR analysis: QSPR studies of the solubility parameter.
    Bagheri M, Golbraikh A.
    SAR QSAR Environ Res; 2012 Jan 27; 23(1-2):59-86. PubMed ID: 22040297
    [Abstract] [Full Text] [Related]

  • 32. Quantitative structure-property relationship for predicting chlorine demand by organic molecules.
    Luilo GB, Cabaniss SE.
    Environ Sci Technol; 2010 Apr 01; 44(7):2503-8. PubMed ID: 20230049
    [Abstract] [Full Text] [Related]

  • 33. 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 05; 604(2):99-106. PubMed ID: 17996529
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  • 34. Quantitative predictions of gas chromatography retention indexes with support vector machines, radial basis neural networks and multiple linear regression.
    Chen HF.
    Anal Chim Acta; 2008 Feb 18; 609(1):24-36. PubMed ID: 18243870
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  • 35. On the prediction of thermal stability of nitroaromatic compounds using quantum chemical calculations.
    Fayet G, Rotureau P, Joubert L, Adamo C.
    J Hazard Mater; 2009 Nov 15; 171(1-3):845-50. PubMed ID: 19616889
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  • 36. Prediction of aqueous solubility of organic compounds based on a 3D structure representation.
    Yan A, Gasteiger J.
    J Chem Inf Comput Sci; 2003 Nov 15; 43(2):429-34. PubMed ID: 12653505
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  • 37. Linear and nonlinear methods in modeling the aqueous solubility of organic compounds.
    Catana C, Gao H, Orrenius C, Stouten PF.
    J Chem Inf Model; 2005 Nov 15; 45(1):170-6. PubMed ID: 15667142
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  • 38. QSPR Approach to Predict Nonadditive Properties of Mixtures. Application to Bubble Point Temperatures of Binary Mixtures of Liquids.
    Oprisiu I, Varlamova E, Muratov E, Artemenko A, Marcou G, Polishchuk P, Kuz'min V, Varnek A.
    Mol Inform; 2012 Jul 15; 31(6-7):491-502. PubMed ID: 27477467
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  • 39. Robust modelling of solubility in supercritical carbon dioxide using Bayesian methods.
    Tarasova A, Burden F, Gasteiger J, Winkler DA.
    J Mol Graph Model; 2010 Apr 15; 28(7):593-7. PubMed ID: 20060347
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  • 40. QSAR modeling of human serum protein binding with several modeling techniques utilizing structure-information representation.
    Votano JR, Parham M, Hall LM, Hall LH, Kier LB, Oloff S, Tropsha A.
    J Med Chem; 2006 Nov 30; 49(24):7169-81. PubMed ID: 17125269
    [Abstract] [Full Text] [Related]

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