Radial basis function neural network-based QSPR for the prediction of critical temperature

Abstract

A QSPR study was performed to develop models that relate the structures of 856 organic compounds to their critical temperatures. Molecular descriptors derived solely from structure were used to represent molecular structures. A subset of the calculated descriptors selected using forward stepwise regression was used in the QSPR models development. Multiple linear regression (MLR) and radial basis function neural networks (RBFNNs) are utilized to construct the linear and nonlinear QSPR models, respectively. The optimal QSPR model was developed based on a 10–33–1 radial basis function neural network architecture using molecular descriptors calculated from molecular structure alone. The root mean square errors in critical temperature predictions were 13.97 K for the whole set, 12.32 K for the training set, and 14.23 K for the prediction set. The prediction results are in good agreement with the experimental value.