DeepCocrystal extends traditional chemical language processing approaches beyond the 'one-molecule-one-property' paradigm, to learn simultaneously from the SMILES strings of pairs of molecules (i.e., API-coformer pairs). In particular, DeepCocrystal uses two separate CNNs to learn 'latent representations' of the input molecular structures (of each API and coformer), and then aggregates this information via a fully-connected neural network, to predict the potential co-crystallization of the input pair (Fig. ). Via Deep-Cocrystal, the co-crystallization potential of any molecular pair is predicted as a number between 0 (negative) and 1 (positive). Every API and coformer pair is represented with multiple SMILES strings . In particular, given a co-crystallization dataset, SMILES from the 'negative' pairs are augmented proportionally to the percentage of 'positive' pairs, and viceversa. This strategy aims to alleviate class imbalance, without artificially incorporating potentially mislabeled information or loosing important information by undersampling.