Given the proportion of 'positive' and 'negative' pairs, for DeepCocrystal we performed a 2:7 (positive:negative) SMILES augmentation, i.e., we represented each molecule belonging to positive pairs with two randomized SMILES, and those belonging to negative pairs with seven randomized SMILES each. This 'augmented' dataset was used to predict co-crystallization with CNNs. To quantify the efficacy of the chosen augmentation, we performed two controls using the same architecture (Fig. ) and different SMILES versions: (a) 'SMI-CNN(can)' relying on a single canonical (univocal) SMILES string for each molecule, and (b) 'SMI-CNN(1:4)', which uses a 1:4 augmentation. SMI-CNN(can) allows to inspect the effect of using multiple SMILES for the same molecule as in DeepCocrystal, while SMI-CNN(1:4) controls for the chosen augmentation level.