This characteristic of SMILES strings can be used for data augmentation, by representing the same molecule in the training set with n different SMILES strings, usually generated at random. SMILES augmentation has been shown beneficial to improve the performance of quantitative structure-activity relationship (QSAR) models , as well as to improve the quality of de novo design models , with the magnitude depending on the structural complexity of the training molecules . The performance improvement of SMILES augmentation plateaus with increasing the number of SMILES per molecule , leading to progressively smaller performance gains with increasing computational cost.