Bioinformatics (Oxford, England) · 2026
Spatial-spectral fusion enables drug repositioning by capturing indirect and long-range associations in biological networks.
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Zhu Xiaobo, Wang Lei, Tang Runzhou, Huang Zhi-An, Huang Yu-An, Tan Feng, Hu Lun, You Zhuhong, Hu Pengwei
Аннотация
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.
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.