Table : Performance comparison between the trained transformers and random-forest models across several metrics. All scores are given as unweighted averages across taste classes which emphasizes minority classes, in our case umami. Area under the receiver operating characteristic (AUROC) values are calculated as one-vs-rest for each taste class. Support refers to the number of compounds out of the test set which were assigned a prediction by the model. Notably, the balanced random forest method, which under-samples the majority class to ensure that all classes are equally represented during training, did not perform better even when considering the more favorable unweighted averages. XGBoost combined with a larger albeit unbalanced dataset yielded superior prediction performance. Unlike the transformer models, which are trained on text-based representations of molecules (i.e. SMILES), the baseline classifiers were trained using a vector representation of molecules known as Morgan fingerprints . Concatenating these fingerprints with an additional set of 15 molecular descriptors that were previously found to be highly correlated with taste did not improve performance. This observation is relatively unsurprising given that these descriptors are nearly exclusively based on adjacency matrices and therefore encode little additional information compared to the atom-radius based Morgan fingerprint.