  Adverse drug-drug interactions (DDIs) remain a leading cause of morbidity and
mortality. Identifying potential DDIs during the drug design process is
critical for patients and society. Although several computational models have
been proposed for DDI prediction, there are still limitations: (1) specialized
design of drug representation for DDI predictions is lacking; (2) predictions
are based on limited labelled data and do not generalize well to unseen drugs
or DDIs; and (3) models are characterized by a large number of parameters, thus
are hard to interpret. In this work, we develop a ChemicAl SubstrucTurE
Representation (CASTER) framework that predicts DDIs given chemical structures
of drugs.CASTER aims to mitigate these limitations via (1) a sequential pattern
mining module rooted in the DDI mechanism to efficiently characterize
functional sub-structures of drugs; (2) an auto-encoding module that leverages
both labelled and unlabelled chemical structure data to improve predictive
accuracy and generalizability; and (3) a dictionary learning module that
explains the prediction via a small set of coefficients which measure the
relevance of each input sub-structures to the DDI outcome. We evaluated CASTER
on two real-world DDI datasets and showed that it performed better than
state-of-the-art baselines and provided interpretable predictions.
