Rethinking Drug-Drug Interaction Modeling as Generalizable Relation Learning
This addresses a critical issue in drug discovery and clinical development for polypharmacy by improving generalization in DDI prediction, though it is an incremental advance over existing methods.
The paper tackled the problem of poor generalization in drug-drug interaction (DDI) prediction to unseen drugs and novel pairs by proposing GenRel-DDI, a relation-centric learning framework, which significantly outperformed state-of-the-art methods, especially on entity-disjoint evaluations.
Drug-drug interaction (DDI) prediction is central to drug discovery and clinical development, particularly in the context of increasingly prevalent polypharmacy. Although existing computational methods achieve strong performance on standard benchmarks, they often fail to generalize to realistic deployment scenarios, where most candidate drug pairs involve previously unseen drugs and validated interactions are scarce. We demonstrate that proximity in the embedding spaces of prevailing molecule-centric DDI models does not reliably correspond to interaction labels, and that simply scaling up model capacity therefore fails to improve generalization. To address these limitations, we propose GenRel-DDI, a generalizable relation learning framework that reformulates DDI prediction as a relation-centric learning problem, in which interaction representations are learned independently of drug identities. This relation-level abstraction enables the capture of transferable interaction patterns that generalize to unseen drugs and novel drug pairs. Extensive experiments across multiple benchmark demonstrate that GenRel-DDI consistently and significantly outperforms state-of-the-art methods, with particularly large gains on strict entity-disjoint evaluations, highlighting the effectiveness and practical utility of relation learning for robust DDI prediction. The code is available at https://github.com/SZU-ADDG/GenRel-DDI.