Sponsor
This work is supported by the Strategic Priority Research Program of the Chinese Academy of Sciences XDB38030300.
Published In
Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM ’24)
Document Type
Article
Publication Date
10-2024
Abstract
Accurately predicting Drug-Drug Interactions (DDIs) is critical to designing effective drug combination therapies. Recently, Artificial Intelligence (AI)-powered DDI prediction approaches have emerged as a new paradigm. However, most existing methods oversimplify the complex hierarchical structure within molecules and overlook the multi-source heterogeneous information external to molecules, limiting their modeling and predictive capabilities. To address this, we propose a Hierarchical Heterogeneous graph learning framework for DDI prediction, namely H2D. H2D employs an internal-toexternal, local-to-global hierarchical perspective, exploiting intramolecular multi-granularity structures and inter-molecular biomedical interactions to mutually enhance across hierarchical levels. Extensive experimental results demonstrate H2D’s effectiveness on three real-world DDI prediction tasks (binary-class, multi-class, and multi-label). In sum, H2D achieves state-of-the-art performance in DDI prediction by leveraging the multi-scale graph structures, opening up new avenues in AI-powered DDI prediction.
Rights
Copyright 2024 held by the owner/author(s). Publication rights licensed to ACM.
Permission to republish here was granted by ACM.
DOI
10.1145/3627673.3679936
Persistent Identifier
https://archives.pdx.edu/ds/psu/42694
Citation Details
Ran Zhang, Xuezhi Wang, Sheng Wang, Kunpeng Liu, Yuanchun Zhou, and Pengfei Wang. 2024. H2D: Hierarchical Heterogeneous Graph Learning Framework for Drug-Drug Interaction Prediction. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM ’24), October 21–25, 2024, Boise, ID, USA. ACM, New York, NY, USA,