Fine-tuning on the curated dataset led FART to a performance already on par with the best tree-based models showing that fine-tuned transformer models are capable of taste prediction given sufficient data, see Table . A common technique to synthetically expand the dataset to improve generalizability is to use SMILES augmentation. By using the fact that the same molecule can be represented by multiple SMILES strings, it is possible to generate such additional non-canonical SMILES for each data point . SMILES augmentation can help improve performance more generally but is particularly useful in ensuring the model generalizes well to input SMILES which are not canonicalized. Indeed, the performance of the unaugmented iteration of FART droped markedly (by about 6 to 10 percentage points across and as well as Supplementary Figure .