For the direct comparison, F1-score (test set) and MCC (test set) of the model from BitterX are 0.918 and 0.842 respectively (Table 1), and the counterparts of the model from BitterPredict are 0.708 and 0.595 respectively (Table 1). It worth mentioning that F1-score and MCC are all calculated based on the data reported in their works. From this perspective, BitterX is much better than BitterPredict. Additionally, the model from BitterX and our models including consensus models and average models are markedly better than the counterpart from BitterPredict (Red sphere in Figure 3). Moreover, one of our consensus models (CM1, Blue sphere in Figure 3) is also a little better than the model from bitterX (Black sphere in Figure 3), the consensus models (CM03, CM05, and CM07) are comparable to the model from BitterX and the consensus models (CM02, CM04, CM06, CM08, and CM09) are slightly inferior to the model from BitterX. It should be noted that the model from BitterPredict is derived from only one random data-splitting scheme, and the model from BitterX is averaged over three random data-splitting schemes. Our consensus models are built by averaging over all the constituent models (19 individual models for CM01 and 5 average models for CM02-CM09), while our average models (AM) in Table S2 are derived by averaging over 19 random data-splitting schemes for KNN, SVM, GBM, and RF or three random data-splitting schemes for DNN2 and DNN3. Therefore, our models in the e-Bitter may offer more robust results in this respect.