Y-randomization test (Rücker et al., 2007) is conducted to inspect the reliability of all the 1,312 models. In this test, the experimentally observed labels (bitter or bitterless) for Dataset-CV are randomly shuffled without changing the total number of bitterants and non-bitterants (Table S3). Worthy of notice is that some labels are still correct due to this random operation. Thus, the newly generated Dataset-CV still contains some true samples but with lot of noise, and its detail is described in Table S3. Subsequently, the five-fold cross-validation on this new dataset is performed with exactly the same molecular descriptors and protocols mentioned in the previous section about the model-training. The best models are determined based on the highest F1-scores assessed on the internal validation dataset during the cross-validation, and further evaluated on the test set (Dataset-Test) without any random shuffling. All the evaluation metrics are collected in Table S4.