Huang et al. developed the first online prediction tool called “BitterX,” which combines Support Vector Machine (SVM) approach (Vapnik, 1995) with the physicochemical descriptors (Huang et al., 2016). In their study, the dataset is composed of 539 bitterants and 539 non-bitterants. Five hundred thirty-nine bitterrants are gathered from the literature and the publicly available BitterDB (Wiener et al., 2012). For 539 non-bitterants, 20 non-bitterants are from their in-house bitterless compounds validated by the experiments, and 519 non-bitterants are the representative structures clustered from the compounds without the tag of “bitter” in the Available Chemicals Directory (ACD) database (http://accelrys.com). Their bitterant prediction model offers the impressive accuracy (91~92%), precision (91~92%), specificity (91~92%), and sensitivity (91~94%) on the test set. However, 519 compounds assumed as the non-bitterants are still not confirmed by the experiments. Thus, the limited number of experimental non-bitterants are the bottleneck for the machine-learning based approach.