KGLGANSynergy: knowledge graph-based local and global attention network for drug synergy prediction

This study provides a more accurate computational tool for predicting drug synergy and introduces a multi-level graph representation learning paradigm, offering new perspectives for biomedical data modeling and showing considerable potential for clinical applications.

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Publication Details

Journal
Journal of Translational Medicine
Published
2025-12-21
DOI
https://doi.org/10.1186/s12967-025-07545-5
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
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article

KGLGANSynergy: knowledge graph-based local and global attention network for drug synergy prediction

Journal of Translational Medicine
Computational Drug Discovery Methods
article

KGLGANSynergy: knowledge graph-based local and global attention network for drug synergy prediction

article en

Abstract

This study provides a more accurate computational tool for predicting drug synergy and introduces a multi-level graph representation learning paradigm, offering new perspectives for biomedical data modeling and showing considerable potential for clinical applications.

Journal of Translational MedicineVol. 24(1)
Henan University (CN), Henan Academy of Sciences (CN)
Openalex Percentile: Top 60%
Computational Drug Discovery Methods
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KGLGANSynergy: knowledge graph-based local and global attention network for drug synergy prediction · Journal of Translational Medicine (2025) | TGRS Research Map | TGRS