Multi-Knowledge Graph and Multi-View Entity Feature Learning for Predicting Drug-Related Side Effects

作者：Ping Xuan, Tianhong Cheng, Hui Cui, Jing Gu, Qiangguo Jin, Tiangang Zhang · 发表于：Journal of Chemical Information and Modeling · 年份：2025 · DOI：10.1021/acs.jcim.5c00136 · 被引用次数：8 · 研究领域：Computational Drug Discovery Methods、Pharmacovigilance and Adverse Drug Reactions、Biomedical Text Mining and Ontologies

Computational prediction of potential drug side effects plays a crucial role in reducing health risks for clinical patients and accelerating drug development. Recent methods have constructed heterogeneous graphs that represent drugs and their side effects, utilizing graph learning strategies such as graph convolutional networks to predict associations between them. However, existing approaches fail to fully exploit the diverse topologies and semantics present in multiple knowledge graphs. We propose MVDSA, a novel multi-view drug-side effect association prediction model. Our approach integrates multiple relationship semantics, local topologies of knowledge graphs, and multi-view features of drug-side effect entity pairs. First, we constructed two knowledge graphs based on drug functional and structural similarity, side effect similarity, and drug-side effect associations. These knowledge graphs capture the topological and semantic connections between drug and side effect entities from diverse perspectives. Second, considering the diverse similarities and associations between entities, we designed a space-sensitive learning strategy where a relation-gated semantic encoder is constructed for each type of relationship. This encoder adaptively adjusts the contribution of each entity feature to the relational semantic representation, facilitating the learning of entity-specific semantic features within each relational space. Third, for the two knowledge graphs, given the multiple ...