Characterizing Bitterness: Identification of Key Structural Features and Development of a Classification Model

This work describes the first approach in the development of a comprehensive classification method for
bitterness of small molecules. The data set comprises 649 bitter and 13 530 randomly selected molecules
from the MDL Drug Data Repository (MDDR) which are analyzed by circular fingerprints (MOLPRINT
2D) and information-gain feature selection. The feature selection proposes substructural features which are
statistically correlated to bitterness. Classification is performed on the selected features via a naïve Bayes
classifier. The substructural features upon which the classification is based are able to discriminate between
bitter and random compounds, and thus we propose they are also functionally responsible for causing the
bitter taste. Such substructures include various sugar moieties as well as highly branched carbon scaffolds.
Cynaropicrine contains a number of the substructural features found to be statistically associated with bitterness
and thus was correctly predicted to be bitter by our model. Alternatively, both promethazine and saccharin
contain fewer of these substructural features, and thus the bitterness in these compounds was not identified.
Two different classes of bitter compounds were identified, namely those which are larger and contain mainly
oxygen and carbon and often sugar moieties, and those which are rather smaller and contain additional
nitrogen and/or sulfur fragments. The classifier is able to predict 72.1% of the bitter compounds. Feature
selection reduces the number of false-positives while also increasing the number of false negatives to 69.5%
of bitter compounds correctly predicted. Overall, the method presented here presents both one of the largest
databases of bitter compounds presently available as well as a relatively reliable classification method.