The feature partial derivative, exactly defined by Equation (8) and Hasegawa et al. (2010) is firstly proposed in the work of Byvatov and Schneider (2004) and is systematically reviewed in the work of Polishchuk (2017). To derive the feature partial derivative of each ECFP bit, the backward finite difference approach is adopted and briefly described as follows (Hasegawa et al., 2010). Firstly, the fingerprint bit nullification is simply done by the replacement of the bit “1” with zero, and then the difference between the predicted probabilities, from the original prediction and the new prediction after the bit nullification, is defined as the feature partial derivative for this fingerprint bit. If the feature partial derivative for one bit is positive, it means that this bit “1” is important to the bitterness of this compound in the positive manner, otherwise, this bit negatively affect the bitterness of this compound. This procedure is repeated for each bit “1” in a compound, thus the feature partial derivative of each ECFP bit in the compound can be derived, which can be done automatically in the e-Bitter program.