Building Machine Learning Small Molecule Melting Points and Solubility Models Using CCDC Melting Points Dataset

Predicting solubility of small molecules is a very difficult
undertaking
due to the lack of reliable and consistent experimental solubility
data. It is well known that for a molecule in a crystal lattice to
be dissolved, it must, first, dissociate from the lattice and then,
second, be solvated. The melting point of a compound is proportional
to the lattice energy, and the octanol–water partition coefficient
(log P) is a measure of the compound’s solvation
efficiency. The CCDC’s melting point dataset of almost one
hundred thousand compounds was utilized to create widely applicable
machine learning models of small molecule melting points. Using the
general solubility equation, the aqueous thermodynamic solubilities
of the same compounds can be predicted. The global model could be
easily localized by adding additional melting point measurements for
a chemical series of interest.