Radial basis function neural network based QSPR for the prediction of critical pressures of substituted benzenes

Abstract

The Quantitative Structure–Property Relationship (QSPR) method is used to develop the correlation between structures of a great number of substituted benzenes and their critical pressure. Molecular descriptors calculated from structure alone were used to represent molecular structures. A subset of the calculated descriptors selected using forward stepwise regression was used in the QSPR model development. Multiple Linear Regression and Radial Basis Function Neural Networks are utilized to construct the linear and non-linear prediction model, respectively. To obtain good prediction ability, both topological structure and training parameters of radial basis function neural networks are optimized. The prediction result agrees well with the experimental value of these properties.