A Comparative Study of Topological and Geometrical Parameters in Estimating Normal Boiling Point and Octanol/Water Partition Coefficient

Basak, Subhash C.; Gute, and Brian D.; Grunwald, Gregory D.

doi:10.1021/ci960024i

Cited by 76 publications

(63 citation statements)

References 36 publications

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“…[6][7][8][9] Many studies on the modeling of log K o/w values using topological, topographic, quantum chemical and other descriptors have been reported where log K o/w values have been the response variable to explore suitability of the descriptors/schemes in QSPR studies. [10][11][12][13][14][15][16] There are some reports about the applications of MLR [17][18][19][20] and artificial neural network, [21][22][23][24] modeling to predict the n-octanol/water partition coefficient of organic compounds. Some of papers, about application of QSPR techniques in the development of a new and simplified approach to prediction of compounds properties were published.…”

Section: Introductionmentioning

confidence: 99%

QSPR study of partition coefficient (Ko/w) of some organic compounds using radial basic function-partial least square (RBF-PLS)

Goudarzi¹,

Goodarzi²

2010

J. Braz. Chem. Soc.

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Neste trabalho, nós introduzimos um novo método da função de base radial por regressão de mínimos quadrados (RBF-PLS) com elevada exatidão e precisão nos estudos quantitativos da relação entre a estrutura-propriedade de compostos orgânicos (QSPR). Três métodos QSPR foram comparados para a predição dos coeficientes de partição no sistema n-octanol-água (K o/w ) (de alguns compostos orgânicos). A regressão linear múltipla (MLR), a regressão parcial dos mínimos quadrados (PLS) e a regressão base radial com funções pelo método de mínimos quadrados (RBF-PLS) foram empregadas para construir os modelos lineares e não-lineares e predizer o valor de K o/w . Os descritores teóricos foram calculados por Dragon e por Gaussian 98 e foram explorados pelas regressões parciais, codificando diferentes aspectos topológicos, geométricos e eletrônicos das estruturas moleculares. A raiz quadrada dos erros médios previstos (RMSEP) para as etapas de testes e da previsão teórica por modelos de MLR, de PLS e de RBF-PLS foram 0,4022; 0,4128; 0,3050; 0,3564; 0,0364 e 0,0533 respectivamente. Também, o erro padrão relativo previsto (RSEP) para os testes e de previsão teórica por MLR, PLS e RBF-PLS foram de 13,24; 13,60; 10,04; 11,74; 1,197 e 1,757 respectivamente. Os dados mostram que o RBF-PLS produziu resultados melhores do que PLS e MLR.In this work, we introduce a new method ability radial basic function-partial least square (RBF-PLS) with high accuracy and precision in QSPR studies. Three quantitative structure-propertty relationship (QSPR) methods have been compared for the prediction of n-octanol-water partition coefficients (K o/w ) of some organic compounds. The multiple linear regressions (MLR), partial least square (PLS) and radial basis function-partial least squares (RBF-PLS) models were employed to construct linear and nonlinear models to predict of K o/w . The theoretical descriptors that calculated by Dragon and Gaussian 98 were explored by stepwise regressions, encoding different aspects of the topological, geometrical and electronic molecular structures. The root means square error of prediction (RMSEP) for training and prediction sets by MLR, PLS and RBF-PLS models were 0.4022, 0.4128, 0.3050, 0.3564, 0.0364 and 0.0533, respectively. Also, the relative standard error of prediction (RSEP) for training and prediction sets by MLR, PLS and RBF-PLS models were 13. 24, 13.60, 10.04, 11.74, 1.197 and 1.757 respectively. The resultant data explained that RBF-PLS produced better results than PLS and MLR.

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Section: Introductionmentioning

confidence: 99%

QSPR study of partition coefficient (Ko/w) of some organic compounds using radial basic function-partial least square (RBF-PLS)

Goudarzi¹,

Goodarzi²

2010

J. Braz. Chem. Soc.

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show abstract

“…[8][9][10][11][12][13][14][15][16][17][18][19] Although the above approach has proved useful in many applications, it has a number of limitations. 11,17,[19][20][21][22] The quantitative relationships between structure and physicochemical properties can be complex and highly nonlinear; thus, determining the optimal analytical form of the QSPR model presents a challenge. Moreover, regression analysis becomes complex and less reliable as the number of descriptors increases.…”

Section: Introductionmentioning

confidence: 99%

“…25,26 As the literature reveals, a major challenge in neural network/QSPR development has been to establish a reliable and practical set of molecular descriptors. 8,11,21,22 As a consequence, most recent studies have explored the development of QSPRs for commonly available physicochemical parameters (e.g., boiling point, heat capacity, density, refractive index) for selected organic compound classes for which accurate and rich data sets are available. 8,17,[19][20][21][22] The use of boiling point data to test the applicability of various molecular descriptors has been particularly popular given the availability of data for large sets of organic chemical classes.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Based Quantitative Structural Property Relations (QSPRs) for Predicting Boiling Points of Aliphatic Hydrocarbons

Espinosa

Yaffe

Cohen

et al. 2000

J. Chem. Inf. Comput. Sci.

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Quantitative structural property relations (QSPRs) for boiling points of aliphatic hydrocarbons were derived using a back-propagation neural network and a modified Fuzzy ARTMAP architecture. With the backpropagation model, the selected molecular descriptors were capable of distinguishing between diastereomers. The QSPRs were obtained from four valance molecular connectivity indices ( 1 v , 2 v , 3 v , 4 v ), a second-order Kappa shape index ( 2 κ), dipole moment, and molecular weight. The inclusion of dipole moment proved to be particularly useful for distinguishing between cis and trans isomers. A back-propagation 7-4-1 architecture predicted boiling points for the test, validation, and overall data sets of alkanes with average absolute errors of 0.37% (1.65 K), 0.42% (1.73 K), and 0.37% (1.54 K), respectively. The error for the test and overall data sets decreased to 0.19% (0.81 K) and 0.31% (1.30 K), respectively, using the modified Fuzzy ARTMAP network. A back-propagation alkene model, with a 7-10-1 architecture, yielded predictions with average absolute errors for the test, validation, and overall data sets of 1.96% (6.79 K), 1.83% (6.45 K), and 1.25% (4.42 K), respectively. Fuzzy ARTMAP reduced the errors for the test and overall data sets to 0.19% (0.73 K) and 0.25% (0.95 K), respectively. The back-propagation composite model for aliphatic hydrocarbons, with a 7-9-1 architecture, yielded boiling points with average absolute errors for the test, validation, and overall set of 1.74% (6.09 K), 1.25% (4.68 K), and 1.37% (4.85 K), respectively. The error for the test and overall data sets using the Fuzzy ARTMAP composite model decreased to 0.84% (1.15 K) and 0.35% (1.35 K), respectively. Performance of the QSPRs, developed from a simple set of molecular descriptors, displayed accuracy well within the range of expected experimental errors and of better accuracy than other regression analysis and neural network-based boiling points QSPRs previously reported in the literature.

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“…In all cases the fuzzy ARTMAP QSPR displayed lower errors and standard deviation. The performance of the backpropagation QSPR was mixed with lower errors, for the same set of compounds, compared with the QSPRs of Gombar and Enslein (1996) and Basak et al (1996) Better performance of the present back-propagation logK ow QSPR model, compared to QSPRs developed from much larger data set, could also be, in part, due to the more heterogeneous character of these larger data sets.…”

Section: Resultsmentioning

confidence: 71%

“…18 QSPRs developed for heterogeneous data sets using topological descriptors [30][31][32] have performed with reported errors comparable to those of group contribution methods. 19,20 For example, the so-called VLOGP model 20 that utilizes a set of over 300 electrotopological-state 32 and kappa shape indices as chemical descriptors was developed using a training set of 6675 compounds (-3.56e logK ow e 7.73) and performed with an average error of 0.201 logK ow units. 20 In recent years, improvements in logK ow QSPRs have been proposed through the use of molecular descriptors derived from semiempirical Molecular Orbital theory (quantum mechanics) calculations.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy ARTMAP and Back-Propagation Neural Networks Based Quantitative Structure−Property Relationships (QSPRs) for Octanol−Water Partition Coefficient of Organic Compounds

Yaffe

Cohen

Espinosa

et al. 2002

J. Chem. Inf. Comput. Sci.

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Quantitative structure-property relationships (QSPRs) for estimating the logarithm octanol/water partition coefficients, logK ow , at 25°C were developed based on fuzzy ARTMAP and back-propagation neural networks using a heterogeneous set of 442 organic compounds. The set of molecular descriptors were derived from molecular connectivity indices and quantum chemical descriptors calculated from PM3 semiempirical MOtheory. Quantum chemical input descriptors include average polarizability, dipole moments, exchange energy, total electrostatic interaction energy, total two-center energy, and ionization potential. The fuzzy ARTMAP/ QSPR performed, for a logK ow range of -1.6 to 7.9, with average absolute errors of 0.03 and 0.14 logK ow for the overall data and test sets, respectively. The optimal 12-11-1 back-propagation/QSPR model, for the same range of logK ow , exhibited larger average absolute errors of 0.23 and 0.27 logK ow for the test and validation data sets, respectively, over the same range of logK ow values. The present results with the fuzzy ARTMAP-based QSPR are encouraging and suggest that high performance logK ow QSPR that encompasses a wider range of chemical groups could be developed, following the present approach, by training with a larger heterogeneous data set.

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A Comparative Study of Topological and Geometrical Parameters in Estimating Normal Boiling Point and Octanol/Water Partition Coefficient

Cited by 76 publications

References 36 publications

QSPR study of partition coefficient (Ko/w) of some organic compounds using radial basic function-partial least square (RBF-PLS)

QSPR study of partition coefficient (Ko/w) of some organic compounds using radial basic function-partial least square (RBF-PLS)

Neural Network Based Quantitative Structural Property Relations (QSPRs) for Predicting Boiling Points of Aliphatic Hydrocarbons

Fuzzy ARTMAP and Back-Propagation Neural Networks Based Quantitative Structure−Property Relationships (QSPRs) for Octanol−Water Partition Coefficient of Organic Compounds

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