Application of QSPR to Mixtures

Abstract

In this paper we report an attempt to apply the QSPR approach for the analysis of data for mixtures. This is an extension of the conventional QSPR approach to the analysis of data for single molecules. The QSPR methodology was applied to a data set of experimental measured density of binary liquid mixtures compiled from the literature. The present study is aimed to develop models to predict the "delta" value of a mixture i.e., deviation of the experimental mixture density (MED) from the ideal, mole-weighted calculated mixture density (MCD). The QSPR was investigated in two perspectives (QMD-I and QMD-II) with respect to the creation of training and test sets. The study resulted in significant ensemble neural network and k-nearest neighbor models having statistical parameters r2, q2(10cv) greater than 0.9, and pred_r2 greater than 0.75. The developed models can be used to predict the delta and hence the density of a new mixture. The QSPR analysis shows the importance of hydrogen bond, polar, shape, and thermodynamic descriptors in determining mixture density, thus aiding in the understanding of molecular interactions important in molecular packing in the mixtures.

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

“…For instance, toxicity of about 100 mixtures toward Photobacterium phosphoreum species can be extracted from papers by Lin et al 251,252 Three datasets of reasonable size (271–411 binary mixtures) related to various properties of liquids were published by Ajmani et al 243,253,254 Vapor-liquid equilibrium data for 101 pure compounds and 261 binary mixtures were compiled by Oprisiu et al 242 using the Korean Thermophysical Properties Databank as a source. 255 550 mixtures composed of 33 individual compounds were investigated by Small et al 256 to discover strong anti-inflammatory combinations.…”

“…Advantages of the additive approach are: (i) the process of descriptor generation is simple and intuitively understandable; and (ii) this approach is not property-oriented, i.e., additive descriptors could be applied (bearing in mind the drawbacks of this methodology) to any investigated activity or property, and, sometimes, this method has shown good results, as in the study of Ajmani et al 254 …”

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

“…However, in recent years several studies have attempted to develop QSPR models to predict non-additive properties (density [1], infinite dilution activity coefficient [2], bubble temperature [3], azeotropic behavior [4], or excess molar volume [5]) of mixtures. There is also very considerable interest in modeling the toxicity of chemical mixtures.…”

“…Further, data for binary mixtures were collected from different sources [1,3,4] and analyzed to demonstrate the usefulness of the new tool. The developed models for several target properties are publicly available at http://ochem.eu/article/23416.…”

Characterization of Mixtures Part 1: Prediction of Infinite‐Dilution Activity Coefficients Using Neural Network‐Based QSPR Models

“…Furthermore, this approach takes no account of interaction effects. An alternative which has been recently examined [3] is to calculate a mole fraction weighted average for each descriptor in the set. This has shown some success in the modeling of density of binary liquid mixtures but of course still neglects interaction effects.…”