Drug target prediction from perturbation transcriptomics via a biological function-guided hypergraph siamese network.

motivationUnderstanding 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.

resultsIn 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.