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However, this integration remains challenging due to the inherent heterogeneity, noise, and bias present in biomedical data. In this study, Knowledge\u2010Guided Drug Relational Predictor (KGDRP), a graph representation learning approach is developed that effectively integrates multimodal biomedical data, including network data containing biological system information, gene expression data, and sequence data that incorporates chemical molecular structures, all within a heterogeneous graph (HG) structure. By incorporating biomedical HG (BioHG) into a heterogeneous graph neural network (HGNN)\u2010based architecture, KGDRP exhibits a remarkable 12% improvement compared to previous methods in real\u2010world screening scenarios. Notably, the biology\u2010informed representation, derived from KGDRP, significantly enhance target prioritization by 26% in drug target discovery. 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