Applications of quantitative structure—property relationships to pharmaceutics

Dearden, John C.

doi:10.1016/0169-7439(94)00020-4

Cited by 21 publications

(9 citation statements)

References 35 publications

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“…It has an important bearing on many problems relating to the transfer or movement of bulk quantities of the liquid. Consequently, viscosity data are becoming increasingly important in studies of the environmental behavior of organic compounds and the quantitative structure−activity relationships (QSARs) of drugs …”

Section: Introductionmentioning

confidence: 99%

“…Consequently, viscosity data are becoming increasingly important in studies of the environmental behavior of organic compounds 2 and the quantitative structure-activity relationships (QSARs) of drugs. 3 In addition, chemical reactions in solution depend also on the viscosity of the solvent as described in Kramers' theory. 4 For physicochemical properties like viscosity, most of the traditional estimation procedures have been based on one of the following three approaches: (1) equations derived from theoretical relationships, usually containing empirical parameters that have to be fitted; (2) additive-constitutive schemes based on atomic groups or bonds within molecules; and (3) linear or multilinear regression equations derived from the correlation of the property of interest with some other properties.…”

Section: Introductionmentioning

confidence: 99%

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Development of Both Linear and Nonlinear Methods To Predict the Liquid Viscosity at 20 °C of Organic Compounds

Suzuki

Ebert

Schüürmann

1997

J. Chem. Inf. Comput. Sci.

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Experimental values for the liquid viscosity (η) at 20 °C ranging from 0.164 mPa‚s (trans-2-pentene) to 1490 mPa‚s (glycerol) have been collected from literature for 361 organic compounds containing C, H, N, O, S, and all halogens. Multiple linear regression (MLR) and two-layer neural network (NN) modeling (one hidden layer) with back-propagation have been applied to derive prediction methods for log η using nine descriptors as input. The analysis includes different partitionings of the data set into training and prediction sets and different numbers of hidden-layer neurons of the neural networks. For the linear and nonlinear models derived from a training set of 237 compounds, squared correlation coefficients of 0.92 and 0.93 as well as root-mean-square errors of 0.17 and 0.16 log units were achieved for a prediction set of 124 compounds, reflecting a reasonable accuracy for a wide range of chemical structures and viscosity values. However, only the NN model was capable of successfully treating glycerol with the maximum viscosity value, which was not possible with the MLR approach and with any other existing estimation scheme.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of Both Linear and Nonlinear Methods To Predict the Liquid Viscosity at 20 °C of Organic Compounds

Suzuki

Ebert

Schüürmann

1997

J. Chem. Inf. Comput. Sci.

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“…The employment of these topological descriptors has proven to be extremely useful in QSAR/QSPR studies giving simple correlations between the selected properties and the molecular structure [39][40]. Multiple regression analysis is often employed in such studies in the hope that it might point to structural factors that influence a particular property.…”

Section: Topological Molecular Descriptorsmentioning

confidence: 99%

Maximum topological distances based indices as molecular descriptors for QSPR

Castro

Tueros

Toropov³

2000

Computers & Chemistry

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“…Key words: acidity constant, multi-way analysis, phenol, quantum topological molecular similarity, quantitative structure property relationship Quantitative structure-activity ⁄ property relationships (QSAR ⁄ QSPR), the essential tools in chemometrics and medicinal chemistry, are mathematical equations relating chemical structure to a wide variety of chemical and biological properties ⁄ activities (1). In traditional QSAR ⁄ QSPR analyses, a vector of molecular descriptors (or predictor variables) is calculated for each molecule.…”

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confidence: 99%

Multi‐way Analysis of Quantum Topological Molecular Similarity Descriptors for Modeling Acidity Constant of Some Phenolic Compounds

Esteki¹,

Hemmateenejad²,

Khayamian³

et al. 2007

Chem Biol Drug Des

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Three-way analyses of quantum topological molecular similarity descriptors were used for quantitative structure property relationship modeling of the acidity constant of some phenol derivatives. A three-way data was built for different molecules by constructing a data matrix for each molecule. The matrix was produced by considering different bonds in each molecule and different descriptors in each bond. The three-way models parallel factor analysis and N-way partial least squares, and two-way models including partial least squares were used for modeling structure-acidity relationships. Comparison of the results showed that the three-way arrays produced more predictive models with lower over-fitting. The bilinear partial least square model resulted in a biased estimation of acidity constant of prediction set with average relative error of prediction of 1.87%, whereas that obtained by parallel factor analysis and N-way partial least squares was near to zero (i.e. -0.41 and -0.33, respectively). Additionally, the three-way methods allowed investigating the significant impact of different bonds and different descriptors using leverages of the parallel factor analysis loadings.

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Applications of quantitative structure—property relationships to pharmaceutics

Cited by 21 publications

References 35 publications

Development of Both Linear and Nonlinear Methods To Predict the Liquid Viscosity at 20 °C of Organic Compounds

Development of Both Linear and Nonlinear Methods To Predict the Liquid Viscosity at 20 °C of Organic Compounds

Maximum topological distances based indices as molecular descriptors for QSPR

Multi‐way Analysis of Quantum Topological Molecular Similarity Descriptors for Modeling Acidity Constant of Some Phenolic Compounds

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