A novel QSPR model for predicting θ (lower critical solution temperature) in polymer solutions using molecular descriptors

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.

Polymer Informatics

“…While it is unclear from the paper which polymer representation was used for the calculation of the connectivity indices, the best regression model (eight parameter model) yields only acceptable predictive power (R 2 = 0.77, R 2 cv = 0.77, s = 34.47 for the training set, R 2 = 0.75 for a test set) [152]. Using the same dataset, Afantitis and co-workers subsequently expanded this work by expanding the descriptor space and providing more rigorous validation [153]. The researchers produced a nine-parameter model, which shows improvements over the equation put forward by Liu et al (R 2 = 0.8860, R 2 cv = 0.8546 for the training set, R 2 = 0.8738 for the test set).…”

Molecular Modeling on Pyrimidine-Urea Inhibitors of TNF-α Production: An Integrated Approach Using a Combination of Molecular Docking, Classification Techniques, and 3D-QSAR CoMSIA

“…If the opposite happens then an acceptable 3D-QSAR model cannot be obtained for the specific modeling method and data. 79,80 ■ RESULTS AND DISCUSSION The previous mentioned computational workflow (Scheme 2) was implemented in order to study the data set of the 51 pyrimidine-urea inhibitors of TNF-α production. In the following sections, the computational workflow is described in detail.…”

A general QSPR model for the prediction of θ (lower critical solution temperature) in polymer solutions with topological indices

“…According to the view of Melagraki et al [3], these methods can be roughly divided into three groups. The first group of models is those methods that have a solid theoretical background but that require vapor-liquid or liquid-liquid experimental data to adjust the unknown parameters, resulting in limited predictive ability [4][5][6][7][8].…”