title: Characterization of Mixtures. Part 2: QSPR Models for Prediction of Excess Molar Volume and Liquid Density Using Neural Networks.
abstract: In our earlier work, we have demonstrated that it is possible to characterize binary mixtures using single component descriptors by applying various mixing rules. We also showed that these methods were successful in building predictive QSPR models to study various mixture properties of interest. Here in, we developed a QSPR model of an excess thermodynamic property of binary mixtures i.e. excess molar volume (V(E) ). In the present study, we use a set of mixture descriptors which we earlier designed to specifically account for intermolecular interactions between the components of a mixture and applied successfully to the prediction of infinite-dilution activity coefficients using neural networks (part 1 of this series). We obtain a significant QSPR model for the prediction of excess molar volume (V(E) ) using consensus neural networks and five mixture descriptors. We find that hydrogen bond and thermodynamic descriptors are the most important in determining excess molar volume (V(E) ), which is in line with the theory of intermolecular forces governing excess mixture properties. The results also suggest that the mixture descriptors utilized herein may be sufficient to model a wide variety of properties of binary and possibly even more complex mixtures.
authors.affiliation: Centre for Molecular Design, Institute of Biomedical and Biomolecular Science, University of Portsmouth, King Henry 1 Street, Portsmouth PO1 2DY, UK, Tel/Fax: +91-20-27292268. subhasha@novaleadpharma.com.|Present Address: NovaLead Pharma Pvt. Ltd. Baner Road, Pune, India. subhasha@novaleadpharma.com.
authors.forename: Subhash
authors.initials: S
authors.lastname: Ajmani
authors.affiliation: AkzoNobel RD&I, Wilton Centre, Wilton, Redcar, TS10 4RF, UK.
authors.forename: Stephen C
authors.initials: SC
authors.lastname: Rogers
authors.affiliation: School of Earth, Atmospheric and Environmental Science, University of Manchester, Oxford Road, Manchester M13 9PL.
authors.forename: Mark H
authors.initials: MH
authors.lastname: Barley
authors.affiliation: AkzoNobel RD&I, Wilton Centre, Wilton, Redcar, TS10 4RF, UK.
authors.forename: Andrew N
authors.initials: AN
authors.lastname: Burgess
authors.affiliation: Centre for Molecular Design, Institute of Biomedical and Biomolecular Science, University of Portsmouth, King Henry 1 Street, Portsmouth PO1 2DY, UK, Tel/Fax: +91-20-27292268.|ChemQuest, Delamere House, 1 Royal Crescent, Sandown, Isle of Wight, PO36 8LZ UK.
authors.forename: David J
authors.initials: DJ
authors.lastname: Livingstone
keywords: Ensemble neural network; Excess molar volume; Mixtures; Neural network; QSPR