To train DeepCocrystal, we collected and manually curated a dataset of experimentally-determined co-crystal structures, from (a) the Cambridge Structural Database and (b) existing co-crystal literature . Moreover, a set of in-house experiments was conducted to measure the cocrystallization of additional molecular pairs. The collected dataset comprises a total of 6632 API-coformer pairs, of which 5240 (79%) are co-crystals ("positive") and 1392 (21%) are physical mixtures ("negative", i.e., no observed co-crystallization). The dataset was randomly divided into training, validation, and 'internal' test sets (80%, 10%, and 10%, respectively) using class-based stratification. This splitting procedure was repeated ten times. Interestingly, data curation by removing non-informative molecular pairs resulted pivotal to avoid model "shortcuts" and memorization (see Materials and Methods). An 'external test set' was curated in-house, which contains 364 molecule pairs (129 are co-crystals and 235 non-co-crystals). The external test set has lower substructure similarity to the training set (Supporting Fig. ) -constituting a more challenging prediction scenario.