The feature importance of ECFP bit is also of particular interest for us. In this work, feature importance of each ECFP bit is derived from the random forest (RF) after the five-fold cross-validation as mentioned above, and feature importance will be automatically linked to the corresponding fingerprint bit “1” and structural feature in our e-Bitter program. However, the feature importance from RF can only tell us whether these features are vital to the bitter/bitterless classification, but cannot inform us whether each ECFP bit “1” in a compound positively or negatively influences the bitterness, which can be described by the concept “feature partial derivative.” (Hasegawa et al., 2010; Marcou et al., 2012; Polishchuk, 2017).