Modeling of non-additive mixture properties using the Online CHEmical database and Modeling environment (OCHEM)

Oprisiu, Ioana; Novotarskyi, Sergii; Tetko, Igor V.

doi:10.1186/1758-2946-5-4

Cited by 49 publications

(45 citation statements)

References 12 publications

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“…proposed models to predict the density and the infinite‐dilution activity coefficient of binary mixtures by combining the molecular descriptors for each single compound, e. g. by mole weighted averaging, to derive mixture descriptors, and correlating them to the property. Several studies were also dedicated to azeotropic mixtures, like the models of Oprisiu et al . to predict their boiling point based on fragment descriptors.…”

Section: Introductionmentioning

confidence: 99%

New QSPR Models to Predict the Flammability of Binary Liquid Mixtures

Fayet

Rotureau

2019

Molecular Informatics

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This paper is dedicated to Prof. Paola Gramatica on the occasion of her retirement.Abstract: New Quantitative Structure-Property Relationships (QSPR) are presented to predict the flash point of binary liquid mixtures, based on more than 600 experimental flash points for 60 binary mixtures. Two models are proposed based on a GA-MLR approach that uses a genetic algorithm (GA) variable selection in multilinear regressions (MLR). In these models, mixtures were characterized by a series of mixture descriptors calculated from various mixture formula combining the molecular descriptors of the single com-pounds constituting the mixtures and their respective molar fractions in the mixture. The best model demonstrated good predictive capabilities with a mean absolute error of only 7.3 8C estimated for an external validation set. Moreover, this model is focused on mixture descriptors applicable to more complex mixtures, i. e. constituted of more than 2 components, and already demonstrated interesting predictions for a series of ternary mixtures.(4 of 8) 1800122 a Details on the exact composition of each set were not available; n D : number of mixture descriptors; n: number of molecules in the whole dataset; n TR : number of molecules in the training set; n EXT : number of molecules in the validation set; NA: not applicable.

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Section: Introductionmentioning

confidence: 99%

New QSPR Models to Predict the Flammability of Binary Liquid Mixtures

Fayet

Rotureau

2019

Molecular Informatics

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“…Oprisiu et al. developed a model for binary mixtures and develop models to predict their non‐additive properties 5. Muratov et al.…”

Section: Introductionmentioning

confidence: 99%

Classification of Mixtures of Chinese Herbal Medicines Based on a Self‐organizing Map (SOM)

Wang

Chen

et al. 2016

Molecular Informatics

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Chinese Herbal Medicines (CHMs) are typically mixtures of compounds and are often categorized into cold and hot according to the theory of Chinese Medicine. This classification is essential for guiding the clinical application of CHMs. In this study, three types of molecular descriptors were used to build models for classification of 59 CHMs with typical cold/hot properties in the training set taken from the original records on properties in China Pharmacopeia as reference. The accuracy and the Matthews correlation coefficient of the models were validated by a test set containing other 56 CHMs. The best model produced the accuracies of 94.92 % and 83.93 % on training set and test set, respectively. The MACCS fingerprint model is robust in predicting hot/cold properties of the CHMs from their major constituting compounds. This work shows how a classification model for data consisting of multi-components can be developed. The derived model can be used for the application of Chinese herbal medicines.

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“…Several studies have also been dedicated to azeotropic mixtures. 25−29 In particular, Oprisiu et al 29 developed several QSPR models to predict the boiling points 25,28 of azeotropic binary mixtures on the basis of fragment descriptors.The flash point (FP) is the temperature at which the vapor above a flammable liquid ignites under the effect of a spark. 30 This property characterizes flammability hazards of liquids and is a key safety issue in the risk assessment of industrial processes and in various regulatory frameworks dedicated to chemicals (for use, storage, and transport).…”

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confidence: 99%

Mixture Descriptors toward the Development of Quantitative Structure–Property Relationship Models for the Flash Points of Organic Mixtures

Gaudin

Rotureau

Fayet

2015

Ind. Eng. Chem. Res.

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Quantitative structure−property relationships (QSPRs) are increasingly used for the prediction of physicochemical properties of pure compounds, but only a few have been developed to predict the properties of mixtures. In this work, a series of existing and new formulas were proposed to derive mixture descriptors for the development of QSPR models for mixtures. These mixture descriptors were used to model the flash points of a series of 435 organic mixture compositions. Multilinear models were obtained using 12 different mathematical formulas, taking into account the linear or nonlinear dependence of the flash point on the concentration of each compound. The best model, issued from the newly proposed (x 1 d 1 + x 2 d 2 ) 2 formula, was a four-parameter model presenting good prediction capabilities (with a mean absolute error in prediction of 10.3°C) compared with existing predictive methods for both mixtures and pure compounds. ■ INTRODUCTIONQuantitative structure−property relationships (QSPRs) are predictive models that allow the prediction of macroscopic properties by correlation with descriptors of the molecular structures of chemicals. 1 These molecular descriptors belong to various categories: 1,2 constitutional, topological, geometrical, or quantum-chemical. Such methods have been largely used for biological activities in the fields of toxicology, 3 ecotoxicology, 4 and pharmaceutics 5,6 and are increasingly used for physicochemical properties. 7−9Various models have been developed for hazardous physicochemical properties 10−19 such as flammability, 10,11 thermal stability, 12−14 and explosibility. 15−19 To date, the QSPR approach has mostly been dedicated to pure compounds, and only a few recent works have been dedicated to mixtures. 20 Ajmani and co-workers proposed various models to predict the densities 21,22 and infinite-dilution activity coefficients 23 of binary mixtures. In these studies, the molecular descriptors for each pure compound were combined, e.g., by mole-weighted averaging, 24 to derive mixture descriptors. These mixture descriptors were then correlated to the property of the studied mixtures. Several studies have also been dedicated to azeotropic mixtures. 25−29 In particular, Oprisiu et al. 29 developed several QSPR models to predict the boiling points 25,28 of azeotropic binary mixtures on the basis of fragment descriptors.The flash point (FP) is the temperature at which the vapor above a flammable liquid ignites under the effect of a spark. 30 This property characterizes flammability hazards of liquids and is a key safety issue in the risk assessment of industrial processes and in various regulatory frameworks dedicated to chemicals (for use, storage, and transport). 31,32 The flash points of pure compounds have been studied in several works with the aim of developing predictive methods taking advantage of the large availability of data. 33,34 Among them, many are based on knowledge of other properties such as the boiling point. 24,35,36 The highest performance was obtained by Carroll ...

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Modeling of non-additive mixture properties using the Online CHEmical database and Modeling environment (OCHEM)

Cited by 49 publications

References 12 publications

New QSPR Models to Predict the Flammability of Binary Liquid Mixtures

New QSPR Models to Predict the Flammability of Binary Liquid Mixtures

Classification of Mixtures of Chinese Herbal Medicines Based on a Self‐organizing Map (SOM)

Mixture Descriptors toward the Development of Quantitative Structure–Property Relationship Models for the Flash Points of Organic Mixtures

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