The performance of the models was evaluated on a subset of 15% (or 2,254 molecules) of the dataset, which had been excluded from the training. All models were evaluated on the same test set for various metrics, see Table . While accuracy measures how a model performs across all five taste categories, other metrics need to be calculated on a per-class basis. For the class "sweet" for example we would evaluate precision (proportion of predicted sweet molecules which are truly sweet), recall (proportion of truly sweet molecules that were also predicted sweet), the F1 score (which combines recall and precision into one number) and the area under the receiver operating characteristic (AUROC, which measures a model's ability to distinguish between classes). As FART is specifically tasked with parallel predictions across four taste categories, we decided to report overall performance using unweighted (macro) averages across the five taste categories. This means that a model cannot compensate bad performance on a minority class (in our case umami) with exceptionally good performance on the other, more common classes. More detailed results for each taste class individually and their weighted averages can be found in the supplementary information (SI).