School of Chemical Engineering, National Technical University of Athens, Athens, Greece
J Mol Model. 2007;13(1):55-64. doi: 10.1007/s00894-006-0125-z.
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.
在这项研究中，我们提出了一个新的模型，该模型使用包含 12 种聚合物和 67 种溶剂的 169 个数据点的数据库来预测θ（下临界溶液温度）。为了对聚合物和溶剂分子进行特征描述，我们考察了许多分子描述符（拓扑、物理化学、空间和电子）。使用消除选择逐步回归方法选择最佳描述符子集。多元线性回归（MLR）作为统计工具，用于探索分子描述符与实验数据之间的潜在相关性。使用留一交叉验证程序、外部测试集验证和 Y-随机化评估技术来测试 MLR 模型的预测精度。最后确定了适用范围，以确定可靠的预测。
