Training FART on this augmented dataset resulted in improved performance and made predictions much more robust towards different SMILES of the same molecule. Furthermore, this augmentation allowed us to construct a confidence metric in which a prediction is only accepted when a consensus is reached across 10 different SMILES representations for the same molecule, see also section 3.2.3. Notably, data augmentation cannot be used to address data imbalance in the training set. We found that when augmenting in an imbalanced manner, i.e. mostly augmenting our minority class of umami and not augmenting our majority class of sweet, FART greedily classified all canonicalized SMILES as sweet. Performance as measured by a one-vs-rest AUROC consistently improved for FART models compared to XGBoost baselines, which suggests that FART learned an expressive latent representation of the input molecules in which taste classes can be more easily distinguished.