Accurate and interpretable drug-drug interaction prediction enabled by knowledge subgraph learning

arXiv:2311.15056v2 Announce Type: replace
Abstract: Background: Discovering potential drug-drug interactions (DDIs) is a long-standing challenge in clinical treatments and drug developments. Recently, deep learning techniques have been developed for DDI prediction. However, they generally require a huge number of samples, while known DDIs are rare.
Methods: In this work, we present KnowDDI, a graph neural network-based method that addresses the above challenge. KnowDDI enhances drug representations by adaptively leveraging rich neighborhood information from large biomedical knowledge graphs. Then, it learns …

abstract arxiv challenge clinical cs.ai cs.lg deep learning deep learning techniques however interactions knowledge prediction samples type work