Admittedly, our work has some limitations. (1) From the perspective of curated data, the experimentally confirmed dataset probably still has some intrinsic noise, since in the experimental taste assessment, the trained panelists have some objective factors such as the individual gene-polymorphism of Tas2Rs, and also have some subjective factors such as the mixed tastes, which are unlikely to be clearly discerned. (2) As for the parameter optimization in the model training process, it is a daunting task to explore the complete parameters combinations, since the parameter spaces for machine-learning methods such as DNN are very large. Thus, in this work only the key parameters are tuned with the grid method. (3) Regarding to the feature selection, feature importance from the random forest method is used as the criterion to select the important features. In this work, only full features, 512, 256, and 128 features are attempted, while the other feature numbers and feature selection methods, which may provide more promising results, are not tried because of the tremendous combination of options and parameters. (4) From the view of consensus models, this strategy will introduce some extra computational-burden. First, different types of ECFP fingerprints should be generated for each compound, and then each constituent model coupled with its corresponding fingerprint affords the prediction result also for each compound, finally the results from all the constituent models will be averaged to make the final prediction. Therefore, this procedure is not extremely fast, especially for the consensus model containing the deep-neuron network. (5) From the function of e-Bitter, this program is only focused on the bitterant classification for the small molecule due to our current research priority.