Radial basis function network-based quantitative structure–property relationship for the prediction of Henry’s law constant Original Research Article

Quantitative structure–property relationship (QSPR) method is used to develop the correlation models between the structures of a great number of organic compounds and their Henry’s law constants in water. Molecular descriptors calculated from structure alone are used to represent molecular structures. A subset of the calculated descriptors, selected using forward stepwise regression is used in the QSPR models development. Multiple linear regression (MLR) and radial basis function networks (RBFNs) are utilized to construct the linear and non-linear prediction model respectively. The optimal QSPR model developed was based on a 10-17-1 RBFNs architecture using molecular descriptors calculated from molecular structure alone. The root mean square errors in log H predictions for the training, test and overall data sets are 0.3023, 0.3121, and 0.3038 log H units, respectively. The prediction result is agreement with the experimental value.