  Drug-drug interaction (DDI) prediction provides a drug combination strategy
for systemically effective treatment. Previous studies usually model drug
information constrained on a single view such as the drug itself, leading to
incomplete and noisy information, which limits the accuracy of DDI prediction.
In this work, we propose a novel multi- view drug substructure network for DDI
prediction (MSN-DDI), which learns chemical substructures from both the
representations of the single drug (intra-view) and the drug pair (inter-view)
simultaneously and utilizes the substructures to update the drug representation
iteratively. Comprehensive evaluations demonstrate that MSN-DDI has almost
solved DDI prediction for existing drugs by achieving a relatively improved
accuracy of 19.32% and an over 99% accuracy under the transductive setting.
More importantly, MSN-DDI exhibits better generalization ability to unseen
drugs with a relatively improved accuracy of 7.07% under more challenging
inductive scenarios. Finally, MSN-DDI improves prediction performance for
real-world DDI applications to new drugs.
