Abstract
In this study, we present a new model that has been developed for the prediction of θ (lower critical solution temperature) using a database of 169 data points that include 12 polymers and 67 solvents. For the characterization of polymer and solvent molecules, a number of molecular descriptors (topological, physicochemical, steric and electronic) were examined. The best subset of descriptors was selected using the elimination selection-stepwise regression method. Multiple linear regression (MLR) served as the statistical tool to explore the potential correlation among the molecular descriptors and the experimental data. The prediction accuracy of the MLR model was tested using the leave-one-out cross-validation procedure, validation through an external test set and the Y-randomization evaluation technique. The domain of applicability was finally determined to identify the reliable predictions.
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Acknowledgements
G.M. wishes to thank the Greek State Scholarship Foundation for a doctoral assistantship. A.A. wishes to thank Cyprus Research Promotion Foundation (grant no. PENEK/ENISX/0603/05) and A.G. Leventis Foundation for their financial support.
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Melagraki, G., Afantitis, A., Sarimveis, H. et al. A novel QSPR model for predicting θ (lower critical solution temperature) in polymer solutions using molecular descriptors. J Mol Model 13, 55–64 (2007). https://doi.org/10.1007/s00894-006-0125-z
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DOI: https://doi.org/10.1007/s00894-006-0125-z