Rodgers et al. employ the Naive Bayes algorithm and circular fingerprint (MOLPRINT 2D, Willett et al., 1998) to classify the bitter/bitterless compounds (Rodgers et al., 2006). The dataset consists of 649 bitterants and 13,530 hypothetical non-bitterants. All the bitterants are from their proprietary database, while 13,530 hypothetical non-bitterants are randomly selected from the MDL Drug Data Repository (MDDR). The prediction model gives the best accuracy, precision, specificity, and sensitivity of 88, 24, 89, and 72% respectively in the five-fold cross-validation. It's the first bitterant prediction model trained with the large dataset. Nevertheless, the bitterless compounds in their study are not experimentally confirmed, and their work didn't provide a practical prediction tool for the users to have a test on their model.