Quantitative structure property relationship models for the prediction of liquid heat capacity
Xiaojun Yao, Botao Fan, Jean Pierre Doucet, Annick Doucet Panaye, Mancang Liu, Ruisheng Zhang, Xiaoyun Zhang, Zhide Hu · QSAR & Combinatorial Science · 2003
Abstract Quantitative Structure‐Property Relationship (QSPR) models based on molecular descriptors derived from molecular structures have been developed for the prediction of liquid heat capacity at 25 °C using a diverse set of 871 organic compounds. The molecular descriptors used to represent molecular structures include constitutional and topological indices and quantum chemical parameters. Forward stepwise regression and radial basis function neural networks (RBFNNs) were used to construct the QSPR models. The root mean square errors in liquid heat capacity predictions for the training, test and overall data sets are 16.857, 18.744 and 17.141 heat capacity units, respectively. The prediction results are in agreement with the experimental values, but the RBFNN model seems to be better than stepwise regression method.