Analysis and artificial neural network prediction of melting properties and ideal mole fraction solubility of cocrystals - Gamidi, Rama Krishna; Rasmuson, Ake C. (Crystal Growth & Design, 2020)
Different artificial neural network (ANN) 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 is needed, besides the identification of the two compounds from which the cocrystal is formed. In total, 30 cocrystal systems of 8 different model components, namely, theophylline, piracetam, gabapentin-lactam, tegafur, nicotinamide, salicylic acid, syringic acid, and 4,4'-bipyridine, with distinct coformers have been chosen as the model systems for the construction of ANN models. In all the cases, 70% of the data points have 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 r(2) values above 0.986. In particular, the models capture how the coformer structure influences the targeted physical properties of cocrystals.