Radial basis function network-based quantitative structure-property relationship for the prediction of Henry's law constant

摘要

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 aloe are used to represent molecular structures. A subset of the calculated descriptors, selected using forward step-wise 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.