QSPR analysis of infinite dilution activity coefficients of chlorinated organic compounds in water

Xu, Jie; Zhang, Hui; Wang, Luoxin; Ye, Wenxiang; Xu, Weilin; Li, Zengchang

doi:10.1016/j.fluid.2009.12.032

Cited by 24 publications

(11 citation statements)

References 30 publications

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“…[37,38] AAC is related to molecular complexity in terms of atom types. [39] According to Eqn 1, increasing AAC value of DHIs leads to decrease in their photolysis half-lives.…”

Section: Interpretation Of Descriptorsmentioning

confidence: 99%

Prediction of photolysis half-lives of dihydroindolizines by genetic algorithm-multiple linear regression (GA-MLR)

Beiknejad

Chaichi

Fatemi

2016

J. Phys. Org. Chem.

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Quantitative structure-property relationship study was carried out for the prediction of photolysis half-lives of dihydroindolizines. Genetic algorithm variable selection was used in the multiple linear regression modeling. Four descriptors including topological (AAC), 2D-Autocorrelations (GATS7m), 3D-MoRSE (Mor19p), and GETAWAY (HATS3p) descriptors were selected by the algorithm, revealing that a hybrid of topologic, geometric, and electronic features of the molecule affects photolysis half-life of studied photochromic compounds. The results showed that the developed model provides statistically significant predictions of photolysis half-lives of dihydroindolizines.

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“…[37,38] AAC is related to molecular complexity in terms of atom types. [39] According to Eqn 1, increasing AAC value of DHIs leads to decrease in their photolysis half-lives.…”

Section: Interpretation Of Descriptorsmentioning

confidence: 99%

Prediction of photolysis half-lives of dihydroindolizines by genetic algorithm-multiple linear regression (GA-MLR)

Beiknejad

Chaichi

Fatemi

2016

J. Phys. Org. Chem.

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“…The QSPR method is based on the assumption that the variation in the behavior of compounds, as expressed by any measured physicochemical properties, can be correlated with numerical changes in structural features of all compounds, termed "molecular descriptors" [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. The advantage of this method lies in the fact that it requires only knowledge of the chemical structure and is not dependent on any experimental properties.…”

Section: Introductionmentioning

confidence: 99%

A simple 2D-QSPR model for the prediction of Setschenow constants of organic compounds

Fan

2016

Maced. J. Chem. Chem. Eng.

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A quantitative structure-property relationship (QSPR) analysis of the Setschenow constants (K salt ) of organic compounds in a sodium chloride solution was carried out using only two-dimensional (2D) descriptors as input parameters. The whole set of 101 compounds was split into a training set of 71 compounds and a validation set of 30 compounds by means of the Kennard and Stones algorithm. A general four-parameter equation, with correlation coefficient (R) of 0.887 and standard error of estimation (s) of 0.031, was obtained by stepwise multilinear regression analysis (MLRA) on the training set. The reliability and robustness of the present model was verified with leave-one-out cross-validation, randomization tests, and the external validation set. All of the descriptors contained in this model are calculated directly from the molecular 2D structures; thus, this model can be used to easily predict the K salt of other compounds not involved in the present dataset.Keywords: QSPR; Setschenow constants; 2D descriptor; multilinear regression analysis ЕДНОСТАВЕН 2D-QSPR-МОДЕЛ ЗА ПРЕДВИДУВАЊЕ НА КОНСТАНТИТЕ НА SETSCHENOW ЗА ОРГАНСКИТЕ СОЕДИНЕНИЈААнализата на квантитативната зависност на структурата и својствата (QSPR) на константите на Setschenow (K salt ) на органските соединенија во раствор од натриум хлорид е извршена користејќи само дводимензионални (2D) дескриптори како влезни параметри. Целото множество од 101 соединение беше поделено во множество за подготовка од 71 соединение и множество за валидација од 30 соединенија според алгоритамот на Kennard и Stones. Од множеството за подготовка со мултилинеарна регресиона анализа MLRA е добиена општа четирипараметарска равенка со коефициент на корелација R = 0,887 и стандардна грешка на процената s = 0,031. Веродостојноста и робусноста на овој модел беше верифицирана со повеќе тестови: вкрстена валидација со испуштање на еден параметар, тест на рандомизација, како и екстерно множество за валидација. Сите дескриптори содржат модел што се пресметува директно од молекулските 2D-структури, и така овој модел може да се користи за едноставно предвидување на K salt на други соединенија што не се вклучени во ова множество на податоци.

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“…Though the melting temperature of fatty acids can be measured experimentally, the ease and the cost effectiveness of the predictive models have caught the interest of researchers over the last decade [6]. Among the existing predictive models is the quantitative structureproperty relationship approach which premises on the correlation between numerical changes in structural features of molecular descriptors and the variation of the behavior of the compounds as expressed by any measured physicochemical properties [8][9][10][11][12]. Few years ago, Guendouzi and Mekelleche predicted the melting point of fatty acids using five well-correlated descriptors out of a pool of 500 descriptors known to affect the melting points [5].…”

Section: Introductionmentioning

confidence: 99%

Estimation of melting points of fatty acids using homogeneously hybridized support vector regression

Owolabi

Zakariya

Olatunji

et al. 2016

Neural Comput & Applic

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This work develops a hybridized support vector regression (HSVR)-based model for accurate estimation of melting points of fatty acids using their molecular weights and the number of carbon-carbon double bond as descriptors. The development of HSVR-based model is characterized with two stages. The first stage involves training and testing SVR using test-set-cross validation technique with molecular weights and the number of carbon-carbon double bond as descriptors, while the second stage utilizes the estimated melting points obtained from the first stage as descriptor for further training and testing of SVR. The proposed hybrid system therefore demonstrates a better predictive and generalization ability than ordinary SVR. Furthermore, the melting points of sixty-two fatty acids estimated using the proposed HSVR-based model show persistence closeness with the experimental values than the results of other existing predictive models for fatty acids melting points estimation such as Guijie et al. model and Guendouzi model. The developed HSVRbased model is also characterized with higher value of coefficient of correlation and lower value of mean absolute error than that of the existing predictive models. Superiority of the developed HSVR-based model over the existing predictive models in terms of the ease of obtaining its descriptors and the accuracy of its estimates is advantageous to unravel estimation challenges associated with determination of fatty acids melting points.

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QSPR analysis of infinite dilution activity coefficients of chlorinated organic compounds in water

Cited by 24 publications

References 30 publications

Prediction of photolysis half-lives of dihydroindolizines by genetic algorithm-multiple linear regression (GA-MLR)

Prediction of photolysis half-lives of dihydroindolizines by genetic algorithm-multiple linear regression (GA-MLR)

A simple 2D-QSPR model for the prediction of Setschenow constants of organic compounds

Estimation of melting points of fatty acids using homogeneously hybridized support vector regression

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