Spatial-Spectral Fusion Enables Drug Repositioning by Capturing Indirect and Long-Range Associations in Biological Networks

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ID: 324529
2026
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Abstract
MOTIVATION: Drug repositioning accelerates clinical translation by identifying new therapeutic indications for approved drugs. However, therapeutic associations in biomolecular networks often exist indirectly, through transitive chains and long-range mechanisms, rather than as directly observed links. Shallow methods are confined to direct similarity and miss such indirect associations, whereas deep graph neural networks suffer from over-smoothing and lose discriminative power in highly connected networks. RESULTS: We propose a spatial-spectral collaborative framework. In the spatial domain, a wave-evolution process propagates similarity from local to global, capturing multi-hop transitive associations while preserving discriminative representations. In the spectral domain, network-specific spectral transforms model global connectivity for long-range dependencies over homogeneous similarity and heterogeneous drug-protein-disease networks, with the two views aligned by contrastive learning. On three benchmarks the method outperforms state-of-the-art baselines on most evaluation metrics; case studies on Alzheimer's and Parkinson's disease and molecular docking confirm its ability to recover non-explicit therapeutic associations. AVAILABILITY: The source code and data are available at https://github.com/Juniper-cola/BIO_SSF. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7202078187 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Xiaobo Zhu, Lei Wang, Runzhou Tang, Zhi-An Huang, Yu-an Huang, Feng Tan, Lun Hu, Zhuhong You, Pengwei Hu
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag553
URL
https://doi.org/10.1093/bioinformatics/btag553
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