Regarding the range of applicability for the proposed model, we conducted an evaluation to assess whether the model's performance exhibited variations based on the similarity between the tested compounds and the chemicals used during the training phase. Accordingly to our previous works , we evaluated the average similarity score between test and training compounds: (i) the Morgan Fingerprints (1024 bits, radius 2) were calculated using RDKit for all the compounds in the dataset; (ii) the Tanimoto similarity index was computed between each molecule in the test set and the previouslydefined fingerprints; (iii) then the average similarity score was calculated by averaging the similarity scores of the 5 most similar couple of compounds. Compounds in the test set have been divided into 10 quartiles according to the average similarity score and the model performance has been evaluated on each quartile separately (see also Supplementary Figure ). Since the performance remained remarkably stable for each similarity quartile, we concluded that the model was able to preserve its performance regardless of the similarity of the query compounds with the ones in the training set, thus ensuring a general applicability domain. This fact can also be attributed to the composition of the training database used for the model, which encompasses three of the five fundamental taste sensations and includes a fourth category, i.e. "other taste", including a wide spectrum of compounds with distinct tastes. As a result, the curated dataset might embrace a substantial portion of the chemical space that underlies the taste sensations of tastants. It is important to underscore that a primary limitation of the current study is the relatively limited number of umami compounds in the training dataset, particularly in comparison to the number of compounds representing other taste sensations. As previously noted in existing literature , augmenting the number of experimentally identified umami samples would not only enhance the predictive performance of the algorithm but also bolster its robustness.