Accurate prediction of synergistic drug combination using a multi-source information fusion framework

作者：Shuting Jin, Huaze Long, Anqi Huang, Jianming Wang, Xuan Yu, Zhiwei Xu, Junlin Xu · 发表于：BMC Biology · 年份：2025 · DOI：10.1186/s12915-025-02302-y · 被引用次数：14 · 研究领域：Bioinformatics and Genomic Networks、Computational Drug Discovery Methods、Machine Learning in Bioinformatics

BACKGROUND: Accurately predicting synergistic drug combinations is critical for complex disease therapy. However, the vast search space of potential drug combinations poses significant challenges for identification through biological experiments alone. Nowadays, deep learning is widely applied in this field. However, most methods overlook the important role of protein-protein interaction networks formed by gene expression products and the pharmacophore information of drugs in predicting drug synergy. RESULTS: We propose MultiSyn, a multi-source information integration method for the accurate prediction of synergistic drug combinations. Specifically, we design a semi-supervised learning framework using an attributed graph neural network to integrate protein-protein interaction networks of gene expression products with multi-omics data, constructing initial cell line representations that incorporate multi-source information. Furthermore, we refine the initial cell line representation by adaptively integrating it with normalized gene expression profiles, enabling the extraction of cell line features that encapsulate global information. In addition, we decompose drugs into fragments containing pharmacophore information based on chemical reaction rules and construct a heterogeneous graph comprising atomic and fragment nodes. To enhance the capture of molecular structural information, we introduce a heterogeneous graph transformer to learn multi-view representations of heterogeneou...