All methods, except for token deletion, achieve a higher validity compared to the baseline without augmentation (Fig. ). The beneficial effect of the augmentation strategies depends on (a) the augmentation fold -the higher, the better in general, and (b) the training set size -the higher, the lower the effect on validity, as previously reported for SMILES enumeration. The validity achieved by token deletion declines with increasing dataset size, owed to the effect of model training with invalid and/or less common SMILES strings (as particularly visible with high p values and augmentation folds). On uniqueness and novelty, fewer differences among methods exist (Supporting Fig. ), and almost all methods achieve values close to 100%. Atom masking yielded lower uniqueness values than the other approaches (up to 38.8 % worse, Supplementary Fig. ), possibly owed the task of reconstructing masked tokens, which might make the model more reliant on reproducing patterns already seen in the training data.