First, we evaluated the ability to learn the 'chemical syntax' of the SMILES language. We evaluated the generates SMILES strings based on: (a) validity, the percentage of SMILES strings that can be mapped back to 'chemically valid' molecules; (b) uniqueness, the percentage of non-duplicated molecules within the sampled set; and (c) novelty, the percentage of de novo designs that are not included in the training sets. For conciseness, here we report the results of 3x and 10x augmentation, while the remaining results can be found in Supplementary Figure . In general, SMILES augmentation with 25000 training molecules did not lead to remarkable changes compared to not using augmentation (Supplementary Figure ), in agreement with existing literature. Varying the perturbation probability p had a moderate but non-negligible effect on the validity of the generated strings (Supporting Fig. ), and little to no effect on the uniqueness and novelty values (Supporting Fig. ). Each method showed optimal probability values to maximize validity (token deletion: p = 0.05 for, random masking and bioisosteric substitution: p = 0.15; functional group masking: p = 0.30 for group masking; Supporting Fig. ), which will be used for the remainder of this work.