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** Case studies of QSPRs obtained by linear modeling **

QSPR models have been developed by six multivariate calibration methods as described in the previous sections. We focus on demonstration of the use of these methods but not on GC aspects. Since the number of variables is much larger than the number of observations, OLS and robust regression cannot be applied directly to the original data set. These methods could only be applied to selected variables or to linear combinations of the variables. [Pg.187]

Building a QSPR model consists of three steps descriptor calculation, descriptor analysis and optimization, and establishment of a mathematical relationship between descriptors and property. [Pg.512]

We know that every QSPR model is limited by tbe data set that is used for building the model. In order to examine the diversity of this data set (the Huuskonen [Pg.500]

Furthermore, QSPR models for the prediction of free-energy based properties that are based on multilinear regression analysis are often referred to as LFER models, especially, in the wide field of quantitative structure-activity relationships (QSAR). [Pg.489]

Optimal descriptor used for the QSPR modeling of the C60 solubility is expressed as [Pg.341]

Castro EA, Toropov AA, Nesterova AI, Nabiev OM (2004) QSPR modeling aqueous solubility of polychlorinated biphenyls by optimization of correlation weights of local and global graph invariants. CEJC 2 500-523. [Pg.349]

A problem of all such linear QSPR models is the fact that, by definition, they cannot account for the nonlinear behavior of a property. Therefore, they are much less successful for log S as they are for all kinds of logarithmic partition coefficients. [Pg.302]

Sivaraman N, Srinivasan TG, Vasudeva Rao PR, Natarajan R (2001) QSPR modelling for solubility of fullerene (CM ) in organic solvents. J. Chem. Inf. Comput. Sci. 41 1067-1074. [Pg.336]

Beck, B., Breindl, A., Qark, T. QM/NN QSPR models with error estimation vapor pressure and log P. J. Chem. Inf. Comput. Sci. 2000, 40,1046-1051. [Pg.403]

Refinement of a QSPR model requires experimental solubilities to train the model. Several models have used the dataset of Huuskonen [44] who sourced experimental data from the AQUASOL [45] and PHYSPROP [46] databases. The original set had a small number of duplicates, which have been removed in most subsequent studies using this dataset, leaving 1290 compounds. When combined, the log Sw [Pg.302]

NN can be used to select descriptors and to produce a QSPR model. Since NN models can take into account nonlinearity, these models tend to perform better for log S prediction than those refined using MLR and PLS. However, to train nonlinear behavior requires significantly more training data that to train linear behavior. Another disadvantage is their black-box character, i.e. that they provide no insight into how each descriptor contributes to the solubility. [Pg.302]

Multiple linear regression (MLR) is a classic mathematical multivariate regression analysis technique [39] that has been applied to quantitative structure-property relationship (QSPR) modeling. However, when using MLR there are some aspects, with respect to statistical issues, that the researcher must be aware of [Pg.398]

Basak, S. C., Mills, D. Use of mathematical structural invariants in the development of QSPR models. MATCH (Commun. Math. Comput. Chem.) 2001, 44, 15-30. [Pg.499]

Shen M, Xiao Y, Golbraikh A, Gombar VK, Tropsha A. Development and validation of k-nearest-neighbor QSPR models of metabolic stability of drug candidates. J Med Chem 2003 46 3013-20. [Pg.375]

Eros D, Keri G, Kovesdi I, Szantai-Kis C, Meszaros G and Orfi L. Comparison of predictive ability of water solubility QSPR models generated by MLR, PLS and ANN methods. Mini Rev Med Chem 2004 4 167-77. [Pg.508]

D descriptors), the 3D structure, or the molecular surface (3D descriptors) of a structure. Which kind of descriptors should or can be used is primarily dependent on the si2e of the data set to be studied and the required accuracy for example, if a QSPR model is intended to be used for hundreds of thousands of compounds, a somehow reduced accuracy will probably be acceptable for the benefit of short processing times. Chapter 8 gives a detailed introduction to the calculation methods for molecular descriptors. [Pg.490]

The process of defining any QSPR model involves three fundamental components (i) a set of descriptors, (ii) a method to select the most appropriate descriptors, and (iii) the experimental data to train and test the model. It is important to note here that none of these components are unique and many models can be [Pg.301]

Molecular dipole moments are often used as descriptors in QPSR models. They are calculated reliably by most quantum mechanical techniques, not least because they are part of the parameterization data for semi-empirical MO techniques. Higher multipole moments are especially easily available from semi-empirical calculations using the natural atomic orbital-point charge (NAO-PC) technique [40], but can also be calculated rehably using ab-initio or DFT methods. They have been used for some QSPR models. [Pg.392]

** Case studies of QSPRs obtained by linear modeling **

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