Characterization of Mixtures. Part 2: QSPR Models for Prediction of Excess Molar Volume and Liquid Density Using Neural Networks

Ajmani, Subhash; Rogers, Stephen C.; Barley, Mark H.; Burgess, A. N.; Livingstone, David J.

doi:10.1002/minf.201000027

Cited by 10 publications

(15 citation statements)

References 9 publications

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“…241 To date, only a few published QSAR studies of mixtures could be considered reliable. 242,243 An interested reader can find detailed descriptions of studies devoted to mixtures and QSAR modeling of their properties elsewhere. 241,244,245 Herein, we will highlight those studies 246,247 that exemplify lack of awareness of some researchers of the best practices of QSAR modeling, which should apply equally to modeling of both individual compounds and chemical mixtures.…”

Section: Novel Applications Of Qsar and Future Trendsmentioning

confidence: 99%

QSAR Modeling: Where Have You Been? Where Are You Going To?

et al. 2014

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Quantitative Structure-Activity Relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss: (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists towards collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.

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Section: Novel Applications Of Qsar and Future Trendsmentioning

confidence: 99%

QSAR Modeling: Where Have You Been? Where Are You Going To?

et al. 2014

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“…In this study, the groups interaction parameters involved in Margules, NRTL or UNIQUAC equations for activity coefficients have been modeled by QSPR. Ajmani et al 17–19. reported QSPR models for infinite‐dilution activity coefficients, excess molar volume and density of liquid binary mixtures using special mixture descriptors which were calculated as mole weighted average using the descriptor value and mole fraction of each pure component in the mixture.…”

Section: Introductionmentioning

confidence: 99%

“…Another question is related to proper external validation of models for mixtures which is less obvious than in classical QSAR. Some efforts have been done by17–19 who reported validation procedure similar to “Points Out” and “Mixture Out” strategies suggested in this work (see Section 1.2). This, however, is not sufficient to assess prediction performance for mixtures containing new compounds.…”

Section: Introductionmentioning

confidence: 99%

QSPR Approach to Predict Nonadditive Properties of Mixtures. Application to Bubble Point Temperatures of Binary Mixtures of Liquids

Oprisiu¹,

Varlamova²,

Muratov³

et al. 2012

Molecular Informatics

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This paper is devoted to the development of methodology for QSPR modeling of mixtures and its application to vapor/liquid equilibrium diagrams for bubble point temperatures of binary liquid mixtures. Two types of special mixture descriptors based on SiRMS and ISIDA approaches were developed. SiRMS-based fragment descriptors involve atoms belonging to both components of the mixture, whereas the ISIDA fragments belong only to one of these components. The models were built on the data set containing the phase diagrams for 167 mixtures represented by different combinations of 67 pure liquids. Consensus models were developed using nonlinear Support Vector Machine (SVM), Associative Neural Networks (ASNN), and Random Forest (RF) approaches. For SVM and ASNN calculations, the ISIDA fragment descriptors were used, whereas Simplex descriptors were employed in RF models. The models have been validated using three different protocols: "Points out", "Mixtures out" and "Compounds out", based on the specific rules to form training/test sets in each fold of cross-validation. A final validation of the models has been performed on an additional set of 94 mixtures represented by combinations of novel 34 compounds and modeling set chemicals with each other. The root mean squared error of predictions for new mixtures of already known liquids does not exceed 5.7 K, which outperforms COSMO-RS models. Developed QSAR methodology can be applied to the modeling of any nonadditive property of binary mixtures (antiviral activities, drug formulation, etc.).

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“…Ajmani et al. 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 .…”

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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Characterization of Mixtures. Part 2: QSPR Models for Prediction of Excess Molar Volume and Liquid Density Using Neural Networks

Cited by 10 publications

References 9 publications

QSAR Modeling: Where Have You Been? Where Are You Going To?

QSAR Modeling: Where Have You Been? Where Are You Going To?

QSPR Approach to Predict Nonadditive Properties of Mixtures. Application to Bubble Point Temperatures of Binary Mixtures of Liquids

New QSPR Models to Predict the Flammability of Binary Liquid Mixtures

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