A general QSPR model for the prediction of θ (lower critical solution temperature) in polymer solutions with topological indices

Xu, Jie; Liu, Li; Xu, Weilin; Zhao, Sanpin; Zuo, Danying

doi:10.1016/j.jmgm.2007.01.004

Cited by 27 publications

(25 citation statements)

References 25 publications

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“…The researchers produced a nine-parameter model, which shows improvements over the equation put forward by Liu et al (R 2 = 0.8860, R 2 cv = 0.8546 for the training set, R 2 = 0.8738 for the test set). Xu et al tested the dataset previously used by Liu and Afantitis using a set of 199 topological Dragon descriptors [154]. This leads to a linear 10-factor model which shows approximately the same predictive power as that developed by Afantitis et al (R 2 = 0.8874, R 2 cv = 0.8658, s = 24.57).…”

Section: Lower Critical Solution Temperature (Lcst)mentioning

confidence: 99%

Polymer Informatics

Adams

2010

Polymer Libraries

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Polymers are arguably the most important set of materials in common use. The increasing adoption of both combinatorial as well as high-throughput approaches, coupled with an increasing amount of interdisciplinarity, has wrought tremendous change in the field of polymer science. Yet the informatics tools required to support and further enhance these changes are almost completely absent. In the first part of the chapter, a critical analysis of the challenges facing modern polymer informatics is provided. It is argued, that most of the problems facing the field today are rooted in the current scholarly communication process and the way in which chemists and polymer scientists handle and publish data. Furthermore, the chapter reviews existing modes of representing and communicating polymer information and discusses the impact, which the emergence of semantic technologies will have on the way in which scientific and polymer data is published and transmitted. In the second part, a review of the use of informatics tools for the prediction of polymer properties and in silico design of polymers is offered.

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Section: Lower Critical Solution Temperature (Lcst)mentioning

confidence: 99%

Polymer Informatics

Adams

2010

Polymer Libraries

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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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“…The quantitative structure-property relationship (QSPR) approach is based on the assumption that the variation of the behavior of the compounds, as expressed by any measured physicochemical properties, can be correlated with numerical changes in structural features of all compounds [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. The advantage of this approach lies in the fact that it requires only the knowledge of the chemical structure and is not dependent on any experimental properties.…”

Section: Introductionmentioning

confidence: 99%