As concerning the “UNIMI set” dataset (23 bitterants and 33 non-bitterants), bitterX obtains the accuracy, precision, specificity, sensitivity, F1-score and MCC of 60, 52, 56, 65%, 0.58 and 0.21 respectively (Table 3), which are lower than their counterparts reported in the work of Huang et al. (2016) BitterPredict offers the accuracy, precision, specificity, sensitivity, F1-score and MCC of 82, 78, 85, 78%, 0.78 and 0.63 respectively reported in the work of Dagan-Wiener et al. (2017) Meanwhile, e-Bitter gives the accuracy, precision, specificity, sensitivity, F1-score and MCC of 68~73%, 57~61%, 55~58%, 87~100%, 0.69~0.75, and 0.47~0.58 respectively considering the different consensus models (Table 3), which are also lower than their counterparts evaluated on our test set (Dataset-Test) in Table S2. Obviously, BitterPredict and e-Bitter (9 consensus models and 96 average models) unanimously outperform BitterX (Figure 5), while BitterPredict exhibits the slightly better performance than e-Bitter in light of F1-score and MCC (Figure 5).