A Data-Driven and Topological Mapping Approach for the A Priori Prediction of Stable Molecular Crystalline Hydrates

Predictions of the structures of stoichiometric, fractional, or non-stoichiometric hydrates of organic molecular crystals is immensely challenging due to the extensive search space of different water contents, host molecular placements throughout the crystal, and internal molecular conformations. However, the dry frameworks of these hydrates, especially for non-stoichiometric or isostructural dehydrates, can often be predicted from a standard anhydrous crystal structure prediction (CSP). Inspired by developments in the field of drug binding, we introduce an efficient data-driven and topologically aware approach for predicting organic molecular crystal hydrate structures through a mapping of water positions within the crystal structures. As such, the method does not require a priori specification of water content and can, therefore, predict stoichiometric, fractional, and non-stoichiometric hydrate structures. This approach, which we term MACH (Mapping of Crystalline Hydrates), establishes a set of rules for systematic determination of favorable positions for water insertion within predicted or experimental crystal structures based on considerations of the chemical features of local environments and void regions. The proposed approach is tested on hydrates of three pharmaceutically relevant compounds that exhibit diverse crystal packing motifs and void environments characteristic of hydrate structures. Overall, we show that this mapping approach introduces a new advance in the efficient performance of hydrate CSP through generation of stable hydrate stoichiometries at low cost and should be considered an integral component for CSP workflows.