Regarding to the “Phytochemical Dictionary” dataset (49 bitterants and 26 non-bitterants), BitterPredict provides the accuracy, precision, specificity, sensitivity, F1-score and MCC of 88, 86, 69, 98%, 0.91 and 0.735 respectively (Table 4, Dagan-Wiener et al., 2017) while e-Bitter predicts that the accuracy, precision, specificity, sensitivity, F1-score and MCC are 85~92%, 85~91%, 69~81%, 94~98%, 0.89~0.94, and 0.67~0.82, which is comparable with the counterparts evaluated on our test set (Dataset-Test) in Table S2. BitterX achieves the accuracy, precision, specificity, sensitivity, F1-score and MCC of 72, 72, 31, 94%, 0.814 and 0.332 respectively (Table 4). Thus for this dataset, all 9 consensus models and 96 average models in the e-Bitter are consistently better than BitterX (Figure 6), while most of the consensus models (CM01, CM02, CM03, CM04, CM05, CM08, and CM09) and some average models show more promising results than BitterPredict based on the F1-score and MCC (Figure 6).