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• W2213261723 endingPage "2055" @default.
• W2213261723 startingPage "2043" @default.
• W2213261723 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, q210cv 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." @default.
• W2213261723 created "2016-06-24" @default.
• W2213261723 creator A5047947874 @default.
• W2213261723 creator A5061224293 @default.
• W2213261723 creator A5071602867 @default.
• W2213261723 creator A5091545479 @default.
• W2213261723 date "2006-07-18" @default.
• W2213261723 modified "2023-10-14" @default.
• W2213261723 title "Application of QSPR to Mixtures" @default.
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• W2213261723 doi "https://doi.org/10.1021/ci050559o" @default.
• W2213261723 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/16995735" @default.
• W2213261723 hasPublicationYear "2006" @default.
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