In this work, we collect only the experimentally confirmed bitterants and non-bitterants. Based on this fully experimental dataset, we adopt the popular Extended-connectivity Fingerprint (ECFP) (Rogers and Hahn, 2010) as the molecular descriptors and propose the consensus voting from the current mainstream machine-learning methods such as KNN, SVM, RF, GBM, and DNN to build the bitterant/non-bitterant classification models. All the models are carefully inspected by the Y-randomization test to ensure their reliability, and some promising models are subsequently selected to construct nine consensus models that are integrated in our program for the bitterant prediction. To aid the food scientists to automatically predict whether the compound of interest is bitter or not, we present a convenient graphic program called “e-Bitter,” which natively implements ECFPs for the automatic generation of the molecular descriptors. More importantly, e-Bitter can intuitively visualize the inter-connected 3D structural feature, feature importance and feature partial derivative for any specific bit “1” in ECFP. At last, the performance and functions of our program compared with other bitterant prediction tools are discussed.