Compared to state-of-the-art classifiers tasked with binary taste prediction (sweet/non-sweet, bitter/non-bitter, sour/nonsour or umami/non-umami), FART consistently performs better despite having been trained on the more challenging task of multi-class prediction, see Table . Note that while the performance metrics were re-calculated for FART based on the respective binary prediction tasks, the test set FART was evaluated on is significantly larger (2254 vs below 500) and more diverse than the homogeneous test sets compiled for binary prediction such as sweet/non-sweet. Therefore, FART has to learn a much broader chemical space and has to embed information for five rather than two labels. Despite this, FART performs superior compared to these state-of-the-art binary classifiers.