Premexotac: Machine learning bitterants predictor for advancing pharmaceutical development

Bitter taste receptors were recently found to be involved in numerous physiological and pathological conditions other than taste and are suggested as potential drug targets. In vivo and in vitro techniques for screening bitterants as ligands come with economical, time and ethic challenges. Therefore, in silico tools can represent a valuable alternative due to their practicality. Yet, the main challenge of already established ligand-based (LB) classifiers is the low number of experimentally confirmed bitterants and non-bitterants. Premexotac models were constructed as a LB bitterants screener, exploring novel combinations of feature extraction, feature selection and learning algorithms as a contrast with the already available screeners. Premexotac came among the top performers, exhibiting a F-1 score up to 81% on external validation. Premexotac identified as well insights on physico-chemical and topological descriptors important for bitter prediction. Among the key insights, important mo-lecular substructures from Extended Connectivity Fingerprints for bitterness classification were identified. Also, the importance of a selection of physicochemical/topological descriptors was ranked using mutual information and it was found that descriptors related to the ramification of the molecular structure and molecular weight came at the top of the ranking. The remaining challenges for improving performance were discussed and stated, widening the LB bitterness prediction outlook.