Analysis and ANN prediction of melting properties and ideal mole fraction solubility of co-crystals

Different Artificial Neural Network models have been developed and examined for prediction of cocrystal properties based on pure component physical properties only. From the molecular weight, melting temperature, melting enthalpy and melting entropy of the pure compounds, the corresponding melting properties of the cocrystals and the cocrystal ideal solubility have been successfully predicted. Notably, no information, whatsoever about the cocrystals are needed, besides the identification of the two compounds from which the cocrystal is formed. In total, thirty co-crystal systems of eight different model components, namely, Theophylline, Piracetam, Gabapentin-lactam, Tegafur, Nicotinamide, Salicylic acid, Syringic acid and 4,4'-Bipyridine with distinct coformer’s has been chosen as the model system’s for the construction of ANN models. In all the cases, 70% of the data points has been used to train the model and the rest were used to test the capability of the model (as a validation set) as selected through a random selection process. The training process was stopped with overall r2 values above 0.986. In particular, the models capture how the coformer structure influences on the targeted physical properties of cocrystals.