  Concomitant administration of drugs can cause drug-drug interactions (DDIs).
Some drug combinations are beneficial, but other ones may cause negative
effects which are previously unrecorded. Previous works on DDI prediction
usually rely on hand-engineered domain knowledge, which is laborious to obtain.
In this work, we propose a novel model, Molecular Substructure-Aware Network
(MSAN), to effectively predict potential DDIs from molecular structures of drug
pairs. We adopt a Transformer-like substructure extraction module to acquire a
fixed number of representative vectors that are associated with various
substructure patterns of the drug molecule. Then, interaction strength between
the two drugs' substructures will be captured by a similarity-based interaction
module. We also perform a substructure dropping augmentation before graph
encoding to alleviate overfitting. Experimental results from a real-world
dataset reveal that our proposed model achieves the state-of-the-art
performance. We also show that the predictions of our model are highly
interpretable through a case study.
