Work

Biomedical Knowledge Graph Embedding with Hierarchical Capsule Network and Rotational Symmetry for Drug-Drug Interaction Prediction

Year: 2025

Type: article

Abstract: The forecasting of Drug-Drug Interactions (DDIs) is essential in pharmacology and clinical practice to prevent adverse drug reactions. Existing approaches, often based on neural networks and knowledge graph embedding, face limitations in modeling correlations among drug features and in handling complex BioKG relations, such as one-to-many, hierarchical, and composite interactions. To address these issues, we propose Rot4Cap, a novel framework that embeds drug entity pairs and BioKG relationships into a four-dimensional vector space, enabling effective modeling of diverse mapping properties and hierarchical structures. In addition, our method integrates molecular structures and drug descriptions with BioKG entities, and it employs capsule network–based attention routing to capture feature correlations. Experiments on three benchmark BioKG datasets demonstrate that Rot4Cap outperforms state-of-the-art baselines, highlighting its effectiveness and robustness.

Source: Symmetry

DOI: 10.3390/sym17111793

Authors: Sensen Zhang, Xia Li, Yang Liu, Peng Bi, Tiangui Hu

Institutions: College of Modern Science and Technology, China Jiliang University, China Jiliang University, Renmin University of China, University of Electronic Science and Technology of China

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,896