文献类型：期刊文献

英文题名：Knowledge Graph Information Bottleneck for Drug-Drug Interaction Prediction

作者：Liu, Shun[1]; He, Gaoqi[1]; Zhang, Kai[2]; Li, Honglin[3]

机构：[1] East China Normal University, School of Computer Science and Technology, China; [2] East China Normal University, School of Computer Science and Technology, Innovation Center for AI and Drug Discovery, China; [3] East China University of Science and Technology, Innovation Center for AI and Drug Discovery, Shanghai Key Laboratory of New Drug Design, East China Normal University, China

年份：2024

外文期刊名：Proceedings of the International Joint Conference on Neural Networks

收录：EI(收录号：20244017122245)

语种：英文

外文关键词：Graph neural networks - Prediction models

摘要：Drug-drug interaction (DDI) prediction is an important but challenging task in drug safety surveillance. With the accumulation of biological data, biomedical knowledge graphs (KGs) become available to model DDIs and related biological mechanisms. However, the presence of substantial noise in large-scale KGs hampers prediction performance and the identification of interpretable biological pathways. To fill the gaps, this paper proposes an information bottleneck-based (IB-based) framework that simultaneously denoises the KG and identifies key entities around drug pairs. Moreover, KG-based prediction methods rarely exploit the structural information of drug molecules. To this end, the proposed framework relates drug structures to IB objectives, together with a unique drug pair-centered readout to fuse molecular information into KG subgraph embeddings. Extensive experimental results and case studies demonstrate the effectiveness and interpretability of the framework. ? 2024 IEEE.