Recently Wiener et al. published a prediction tool named “BitterPredict,” which adopts 12 basic physiochemical descriptors and 47 Schrödinger QikProp descriptors (Dagan-Wiener et al., 2017). In this work, the classification method is the adaptive ensemble machine-learning method “Adaptive Boosting” (AdaBoost), whose advantage is that this method is simple, fast, less susceptible to the overfitting. Meanwhile, the dataset is larger than the counterpart in Huang et al. and comprises 691 bitterants and 1,917 non-bitterants. The bitterants are mainly from their BitterDB (Wiener et al., 2012), and the work of Rojas et al. (2016) The non-bitterants are composed of 1,360 non-bitter flavors, 336 sweeteners, 186 tasteless compounds, and 35 non-bitter molecules (Eric Walters, 1996; Arnoldi et al., 1997; Ley et al., 2006; Rojas et al., 2016). The last four sets of compounds are experimentally confirmed, whereas 1,360 non-bitter flavors are gathered from the Fenroli's Handbook of Flavor Ingredients (Burdock, 2004), and are hypothetically defined as the non-bitterants if the word “bitter” is not explicitly mentioned in the description section of each compound in the book (Burdock, 2004). Their prediction model gives the accuracy (83%), precision (66%), specificity (86%), and sensitivity (77%) on the test set. Nevertheless, majorities of non-bitterants (1,360 non-bitter flavors) are still hypothetical. In addition, BitterPredict works in the environment of commercial MATLAB package and requires the commercial Schrödinger software to generate molecular descriptors, which will hamper the extensive test by the users. So far, users can only send the data to authors for the prediction.