Quantitative Structure–Property Relationships (QSPR)

Jurs, Peter C.

doi:10.1002/0470845015.cqa007m

Cited by 20 publications

(14 citation statements)

References 60 publications

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“…One of the best methods doing this task is to utilize Quantitative Structure Property Relationship (QSPR) method. 1 In QSPR method, molecular properties are predicted by using some analytical solutions, which are expressed as a function of various molecular descriptors derived from molecular structures. There are many different types of molecular descriptors:…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Physicochemical Properties of Organic Molecules Using Semi-Empirical Methods

Kim¹,

Cho²,

Kim³

et al. 2013

Bulletin of the Korean Chemical Society

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Prediction of physicochemical properties of organic molecules is an important process in chemistry and chemical engineering. The MSEP approach developed in our lab calculates the molecular surface electrostatic potential (ESP) on van der Waals (vdW) surfaces of molecules. This approach includes geometry optimization and frequency calculation using hybrid density functional theory, B3LYP, at the 6-31G(d) basis set to find minima on the potential energy surface, and is known to give satisfactory QSPR results for various properties of organic molecules. However, this MSEP method is not applicable to screen large database because geometry optimization and frequency calculation require considerable computing time. To develop a fast but yet reliable approach, we have re-examined our previous work on organic molecules using two semi-empirical methods, AM1 and PM3. This new approach can be an efficient protocol in designing new molecules with improved properties.

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Section: Introductionmentioning

confidence: 99%

“…4,5 ESP is given rigorously by Eq. (1). (1) where, V(r), Z A , and ρ(r') refer to ESP values at distance r, charge on nucleus A, and the electron density of the molecule, respectively.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Physicochemical Properties of Organic Molecules Using Semi-Empirical Methods

Kim¹,

Cho²,

Kim³

et al. 2013

Bulletin of the Korean Chemical Society

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“…Errors in QSAR/QSPR development and use appear and propagate as a consequence of failures to perform certain operations during regression analysis. These failures become rather apparent when basic principles of linear regression analysis are discussed, such as those one can find in the basic statistical literature [3][4][5][6][7][8][9][10][11] and QSAR/QSPR [1,2,[12][13][14][15][16][17][18][19][20] publications. Twelve concepts related to various types of failures will be discussed in this work, using the following nomenclature.…”

Section: Introductionmentioning

confidence: 99%

Is your QSAR/QSPR descriptor real or trash?

Kiralj

Ferreira

2010

Journal of Chemometrics

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The sign change problem in quantitative structure-activity relationship (QSAR), quantitative structure-property relationship (QSPR) and related studies is the controversy related to the signs of correlation coefficients and regression coefficients of a descriptor in univariate and multivariate regressions, before and after the data split. Among 50 investigated regression models with 227 descriptors extracted from the literature, the sign change problem was shown to have a very high frequency, according to four new criteria proposed in this work for its assessment. The sign change problem can be substantially reduced and even eliminated for a given dataset by statistically based variable selection and by checking for the sign change problem before model validation and interpretation. Knowing the fundamentals of statistics related to the sign change problem, its identification and understanding aid in finding effective means to remedy regression models with this deficiency.

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“…A quantitative structure–activity relationship (QSAR) is to correlate experimental quantities such as biological activities or binding constants with various descriptors available for the reagents, and several approaches have been suggested 3. The design of new materials also requires a similar approach—quantitative structure‐property relationship (QSPR) 4. In QSPR work, physicochemical properties of materials are correlated with structural and/or electronic descriptors.…”

Section: Introductionmentioning

confidence: 99%

Prediction of physicochemical properties of organic molecules using van der Waals surface electrostatic potentials

et al. 2004

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The generalized interaction properties function (GIPF) methodology developed by Politzer and coworkers, which calculated molecular surface electrostatic potential (MSESP) on a density envelope surface, was modified by calculating the MSESP on a much simpler van der Waals (vdW) surface of a molecule. In this work, vdW molecular surfaces were obtained from the fully optimized structures confirmed by frequency calculations at B3LYP/6-31G(d) level of theory. Multiple linear regressions for normal boiling point, heats of vaporization, heats of sublimation, heats of fusion, liquid density, and solid density were performed using GIPF variables from vdW model surface. Results from our model are compared with those from Politzer and coworkers. The surface-dependent beta (and gamma) values are dependent on the surface models but the surface-independent alpha and regression coefficients (r) are constant when vdW surface and density surface with 0.001 a.u. contour value are compared. This interesting phenomenon is explained by linear dependencies of GIPF variables.

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Quantitative Structure–Property Relationships (QSPR)

Cited by 20 publications

References 60 publications

Prediction of Physicochemical Properties of Organic Molecules Using Semi-Empirical Methods

Prediction of Physicochemical Properties of Organic Molecules Using Semi-Empirical Methods

Is your QSAR/QSPR descriptor real or trash?

Prediction of physicochemical properties of organic molecules using van der Waals surface electrostatic potentials

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