Neural Network Based on Quantum Chemistry for Predicting Melting Point of Organic Compounds

The melting points of organic compounds were estimated using a combined method that includes a backpropagation neural network and quantitative structure property relationship (QSPR) parameters in quantum chemistry. Eleven descriptors that reflect the intermolecular forces and molecular symmetry were used as input variables. QSPR parameters were calculated using molecular modeling and PM3 semi-empirical molecular orbital theories. A total of 260 compounds were used to train the network, which was developed using MatLab.Then, the melting points of 73 other compounds were predicted and results were compared to experimental data from the literature. The study shows that the chosen artificial neural network and the quantitative structure property relationships method present an excellentalternative for the estimation of the melting point of an organic compound, with average absolute deviation of 5%.