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Journal Abstract Search

157 related items for PubMed ID: 31035591

  • 1. 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
    [Abstract] [Full Text] [Related]

  • 2. Prediction of Lower Flammability Limits for Binary Hydrocarbon Gases by Quantitative Structure-A Property Relationship Approach.
    Pan Y, Ji X, Ding L, Jiang J.
    Molecules; 2019 Feb 19; 24(4):. PubMed ID: 30791456
    [Abstract] [Full Text] [Related]

  • 3. New QSPR Models to Predict the Flammability of Binary Liquid Mixtures.
    Fayet G, Rotureau P.
    Mol Inform; 2019 Aug 19; 38(8-9):e1800122. PubMed ID: 30653824
    [Abstract] [Full Text] [Related]

  • 4. Predicting the auto-ignition temperatures of organic compounds from molecular structure using support vector machine.
    Pan Y, Jiang J, Wang R, Cao H, Cui Y.
    J Hazard Mater; 2009 May 30; 164(2-3):1242-9. PubMed ID: 18952371
    [Abstract] [Full Text] [Related]

  • 5. 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 30; 31(6-7):491-502. PubMed ID: 27477467
    [Abstract] [Full Text] [Related]

  • 6. Prediction of auto-ignition temperatures of hydrocarbons by neural network based on atom-type electrotopological-state indices.
    Pan Y, Jiang J, Wang R, Cao H, Zhao J.
    J Hazard Mater; 2008 Sep 15; 157(2-3):510-7. PubMed ID: 18280036
    [Abstract] [Full Text] [Related]

  • 7. Predicting Thermal Decomposition Temperature of Binary Imidazolium Ionic Liquid Mixtures from Molecular Structures.
    He H, Pan Y, Meng J, Li Y, Zhong J, Duan W, Jiang J.
    ACS Omega; 2021 May 25; 6(20):13116-13123. PubMed ID: 34056461
    [Abstract] [Full Text] [Related]

  • 8. A comprehensive QSPR model for dielectric constants of binary solvent mixtures.
    Soltanpour S, Shahbazy M, Omidikia N, Kompany-Zareh M, Baharifard MT.
    SAR QSAR Environ Res; 2016 Mar 25; 27(3):165-181. PubMed ID: 26911475
    [Abstract] [Full Text] [Related]

  • 9. Neural network-based prediction of auto-ignition temperature of ternary mixed liquids.
    Guo B, Cheng Z, Hu S.
    Heliyon; 2024 Apr 15; 10(7):e28713. PubMed ID: 38596097
    [Abstract] [Full Text] [Related]

  • 10. QSPR estimation models of normal boiling point and relative liquid density of pure hydrocarbons using MLR and MLP-ANN methods.
    Roubehie Fissa M, Lahiouel Y, Khaouane L, Hanini S.
    J Mol Graph Model; 2019 Mar 15; 87():109-120. PubMed ID: 30537641
    [Abstract] [Full Text] [Related]

  • 11. Application of Random Forest and Multiple Linear Regression Techniques to QSPR Prediction of an Aqueous Solubility for Military Compounds.
    Kovdienko NA, Polishchuk PG, Muratov EN, Artemenko AG, Kuz'min VE, Gorb L, Hill F, Leszczynski J.
    Mol Inform; 2010 May 17; 29(5):394-406. PubMed ID: 27463195
    [Abstract] [Full Text] [Related]

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  • 13. QSPR Models to Predict Thermodynamic Properties of Cycloalkanes Using Molecular Descriptors and GA-MLR Method.
    Joudaki D, Shafiei F.
    Curr Comput Aided Drug Des; 2020 May 17; 16(1):6-16. PubMed ID: 30827257
    [Abstract] [Full Text] [Related]

  • 14. A quantitative structure property relationship for prediction of solubilization of hazardous compounds using GA-based MLR in CTAB micellar media.
    Ghasemi JB, Abdolmaleki A, Mandoumi N.
    J Hazard Mater; 2009 Jan 15; 161(1):74-80. PubMed ID: 18456399
    [Abstract] [Full Text] [Related]

  • 15. Application of GA-MLR for QSAR Modeling of the Arylthioindole Class of Tubulin Polymerization Inhibitors as Anticancer Agents.
    Ahmadi S, Habibpour E.
    Anticancer Agents Med Chem; 2017 Jan 15; 17(4):552-565. PubMed ID: 27528182
    [Abstract] [Full Text] [Related]

  • 16. 2D Quantitative structure-property relationship study of mycotoxins by multiple linear regression and support vector machine.
    Khosrokhavar R, Ghasemi JB, Shiri F.
    Int J Mol Sci; 2010 Aug 31; 11(9):3052-68. PubMed ID: 20957079
    [Abstract] [Full Text] [Related]

  • 17. Statistical external validation and consensus modeling: a QSPR case study for Koc prediction.
    Gramatica P, Giani E, Papa E.
    J Mol Graph Model; 2007 Mar 31; 25(6):755-66. PubMed ID: 16890002
    [Abstract] [Full Text] [Related]

  • 18. New QSPR equations for prediction of aqueous solubility for military compounds.
    Muratov EN, Kuz'min VE, Artemenko AG, Kovdienko NA, Gorb L, Hill F, Leszczynski J.
    Chemosphere; 2010 May 31; 79(8):887-90. PubMed ID: 20233619
    [Abstract] [Full Text] [Related]

  • 19. QSPR Modeling of Liquid-liquid Equilibria in Two-phase Systems of Water and Ionic Liquid.
    Klimenko KO, Inês JM, Esperança JMSS, Rebelo LPN, Aires-de-Sousa J, Carrera GVSM.
    Mol Inform; 2020 Sep 31; 39(9):e2000001. PubMed ID: 32469147
    [Abstract] [Full Text] [Related]

  • 20. Application of quantitative structure-property relationship analysis to estimate the vapor pressure of pesticides.
    Goodarzi M, Coelho Ldos S, Honarparvar B, Ortiz EV, Duchowicz PR.
    Ecotoxicol Environ Saf; 2016 Jun 31; 128():52-60. PubMed ID: 26890190
    [Abstract] [Full Text] [Related]

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