  In recent years, the development of Artificial Intelligence (AI) has offered
the possibility to tackle many interdisciplinary problems, and the field of
chemistry is not an exception. Drug analysis is crucial in drug discovery,
playing an important role in human life. However, this task encounters many
difficulties due to the wide range of computational chemistry methods. Drug
analysis also involves a massive amount of work, including determining taste.
Thus, applying deep learning to predict a molecule's bitterness is inevitable
to accelerate innovation in drug analysis by reducing the time spent. This
paper proposes an artificial neural network (ANN) based approach (EC-ANN) for
the molecule's bitter prediction. Our approach took the SMILE (Simplified
molecular-input line-entry system) string of a molecule as the input data for
the prediction, and the 256-bit ECFP descriptor is the input vector for our
network. It showed impressive results compared to state-of-the-art, with a
higher performance on two out of three test sets according to the experiences
on three popular test sets: Phyto-Dictionary, Unimi, and Bitter-new set [1].
For the Phyto-Dictionary test set, our model recorded 0.95 and 0.983 in
F1-score and AUPR, respectively, depicted as the highest score in F1-score. For
the Unimi test set, our model achieved 0.88 in F1-score and 0.88 in AUPR, which
is roughly 12.3% higher than the peak of previous models [1, 2, 3, 4, 5].
