The amount and quality of the training data are key ingredients to successful language modeling . Using multiple representations of the same entity (data augmentation) is one of the strategies for deep learning to work in a small data regime and obtain generalizing models, i.e., to have a chemically meaningful understanding of the training data . To apply data augmentation, we leveraged the nonunivocal property of SMILES string; multiple valid SMILES strings representing the same molecular graph can be obtained by starting the string from any nonhydrogen atom in a molecule (Fig. ). We compared the effect of model training with two augmentation levels (10-fold and 20-fold) on the generated SMILES strings in terms of (1) validity, i.e., percentages of SMILES strings that can be translated back to molecular graphs; (2) uniqueness, which is the percentage of nonduplicated SMILES strings; and ( For each augmentation level, the chemical language model was trained for 10 epochs, with an epoch meaning one pass over all of the training data (Fig. ). We observed that augmenting the training data was beneficial in terms of all indices compared to the nonaugmented scenario (Table ). However, 20-fold augmentation did not further improve the results obtained with 10-fold augmentation (Table ).