Quantitative Structure-Property Relationship Modeling of Diverse Materials Properties

被引:412
作者
Le, Tu [1 ]
Epa, V. Chandana [2 ]
Burden, Frank R. [1 ]
Winkler, David A. [1 ,3 ]
机构
[1] CSIRO Mat Sci & Engn, Clayton 3169, Australia
[2] CSIRO Mat Sci & Engn, Parkville, Vic 3052, Australia
[3] Monash Inst Pharmaceut Sci, Parkville, Vic 3052, Australia
关键词
GLASS-TRANSITION TEMPERATURES; DILUTION ACTIVITY-COEFFICIENTS; ARTIFICIAL NEURAL-NETWORKS; MOLECULAR-FIELD ANALYSIS; HIV-1 PR INHIBITORS; IONIC LIQUIDS; QSAR MODELS; COMBINATORIAL LIBRARIES; FIBRINOGEN ADSORPTION; OPTIMAL DESCRIPTORS;
D O I
10.1021/cr200066h
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
A study was conducted to demonstrate the most commonly used predictive quantitative structure-property relationship (QSPR) modeling methods and their applications to materials design. QSPR methods were based on the hypothesis that changes in molecular structure were reflected in changes in observed macroscopic properties of materials. QSPR modeling was a supervised learning method that extracted the complex relationships between the microscopic structure and properties of materials and their macroscopic properties. The key requirement for QSPR modeling was a reliable data set of molecules or materials whose microscopic structures and properties were well-defined along with their measured macroscopic properties of interest. The reliability of the experimental property chosen to be modeled was important, as it was one of the factors that determined the stability and predictivity of models.
引用
收藏
页码:2889 / 2919
页数:31
相关论文
共 203 条
  • [1] Correlation and prediction of the solubility of Buckminster-fullerene in organic solvents; estimation of some physicochemical properties
    Abraham, MH
    Green, CE
    Acree, WE
    [J]. JOURNAL OF THE CHEMICAL SOCIETY-PERKIN TRANSACTIONS 2, 2000, (02): : 281 - 286
  • [2] Prediction of high weight polymers glass transition temperature using RBF neural networks
    Afantitis, A
    Melagraki, G
    Makridima, K
    Alexandridis, A
    Sarimveis, H
    Iglessi-Markopoulou, O
    [J]. JOURNAL OF MOLECULAR STRUCTURE-THEOCHEM, 2005, 716 (1-3): : 193 - 198
  • [3] Aksyonova T.I., 2003, SYSTEMS ANAL MODELIN, V43, P1331
  • [4] Insights into Sonogashira cross-coupling by high-throughput kinetics and descriptor modeling
    an der Heiden, Markus R.
    Plenio, Herbert
    Immel, Stefan
    Burello, Enrico
    Rothenberg, Gadi
    Hoefsloot, Huub C. J.
    [J]. CHEMISTRY-A EUROPEAN JOURNAL, 2008, 14 (09) : 2857 - 2866
  • [5] [Anonymous], 2007, APPL CHEMOMETRICS SC
  • [6] [Anonymous], DRAGON SOFTWARE TALE
  • [7] Armand M, 2009, NAT MATER, V8, P621, DOI [10.1038/NMAT2448, 10.1038/nmat2448]
  • [8] Predictive modeling of electrocatalyst structure based on structure-to-property correlations of X-ray photoelectron spectroscopic and electrochemical measurements
    Artyushkova, Kateryna
    Pylypenko, Svitlana
    Olson, Tim S.
    Fulghum, Julia E.
    Atanassov, Plamen
    [J]. LANGMUIR, 2008, 24 (16) : 9082 - 9088
  • [9] SOLUBILITIES OF SOLIDS AND LIQUIDS OF LOW VOLATILITY IN SUPERCRITICAL CARBON-DIOXIDE
    BARTLE, KD
    CLIFFORD, AA
    JAFAR, SA
    SHILSTONE, GF
    [J]. JOURNAL OF PHYSICAL AND CHEMICAL REFERENCE DATA, 1991, 20 (04) : 713 - 756
  • [10] Cross-validation as the objective function for variable-selection techniques
    Baumann, K
    [J]. TRAC-TRENDS IN ANALYTICAL CHEMISTRY, 2003, 22 (06) : 395 - 406