Drug Target Prediction from Perturbation Transcriptomics via a Biological Function-Guided Hypergraph Siamese Network

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ID: 324306
2026
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Abstract
MOTIVATION: Understanding how small molecules modulate cellular states remains a critical challenge in drug discovery. The advent of perturbation transcriptomics offers new avenues for elucidating drug-target interactions by capturing cellular transcriptional responses to perturbations. RESULTS: In this study, we propose BioHSNet, a biological function-guided hypergraph siamese network for inferring drug-target interactions from perturbation transcriptomics. BioHSNet utilizes hyperedge representations of functionally grouped gene expression to capture higher-order functional relationships, and integrates compound structural information into the model to bridge chemical structure and functional response. Experimental results demonstrate that BioHSNet outperforms other transcriptome-based methods on the Broad Institute's L1000 datasets, particularly in cold start scenarios. The case study further demonstrates its practical utility for target prediction and drug screening. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/Zxinyizhang/BioHSNet.
Reference Key
openalex_W7197038184 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Xinyi Zhang, Xinliang Sun, Jiuxu Yang, Min Li
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Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag475
URL
https://doi.org/10.1093/bioinformatics/btag475
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