A Knowledge-Guided Graph Learning Approach Bridging Phenotype- and Target-Based Drug Discovery.

ABSTRACT: Discovering therapeutic molecules requires the integration of both phenotype-based drug discovery (PDD) and target-based drug discovery (TDD). However, this integration remains challenging due to the inherent heterogeneity, noise, and bias present in biomedical data. In this study, Knowledge-Guided 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)-based architecture, KGDRP exhibits a remarkable 12% improvement

SUBMITTER: Ye Q 

PROVIDER: S-EPMC12021103 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature