Current commonly-used computational methods for the bitterant prediction are categorized by Bahia et al. which are listed as follows: (Bahia et al., 2018) structure-based method (Floriano et al., 2006; Brockhoff et al., 2010; Singh et al., 2011; Tan et al., 2011; Marchiori et al., 2013; Sandal et al., 2015; Acevedo et al., 2016; Karaman et al., 2016; Suku et al., 2017), ligand-based method (Roland et al., 2013, 2015; Levit et al., 2014) and machine-learning based method (Rodgers et al., 2006; Huang et al., 2016; Dagan-Wiener et al., 2017). Structure-based method requires the 3D structures of Tas2Rs, whose crystal structures still remain unresolved. In contrast, ligand-based method approach such as the 3D-pharmacophore method still works even in the absence of 3D structures of Tas2Rs. Both methods work well for the particular bitter-taste receptor, Nevertheless, a compound that cannot activate one specific Tas2R could still trigger the bitter taste via stimulating the other 24 hTas2Rs. Thus, both methods are not suitable for the general classification of bitterant/non-bitterant. Machine-learning based approach can effectively circumvent the aforementioned problems and can directly predict the bitter or bitterless compounds (Rodgers et al., 2005, 2006; Huang et al., 2016; Dagan-Wiener et al., 2017; Bahia et al., 2018). In this emerging method, certain experimental dataset including both the bitter and bitterless compounds is employed to establish the prediction model, while the target information of the bitter compound is not necessary, which confers the unique advantage on this method.