A Topological Deep Knowledge Graph Approach for Protein Interaction: Sheaf Neural Networks in Drug Discovery

Protein-protein interaction (PPI) prediction serves as a cornerstone in drug discovery, facilitating the identification of therapeutic targets, elucidation of molecular mechanisms, and evaluation of drug efficacy. However, the inherent complexity of biological systems and the integration of heterogeneous, high-dimensional data pose significant computational challenges. Recent advances in Graph Neural Networks (GNNs) have shown promise in modeling biological networks, yet they struggle to capture higher-order dependencies and effectively integrate diverse modalities such as protein sequences, 3D structures, and functional annotations. To address these limitations, a unified framework is proposed that aligns Sheaf Neural Networks (SNNs) with Knowledge Graphs (KGs). SNNs extend GNNs by incorporating sheaf theory, enabling the representation of intricate, multi-dimensional interactions and improving the expressiveness of biological systems. Complementing this, KGs encode structured relationships between proteins and interaction data, enhancing predictive performance and knowledge integration. Comprehensive ablation studies demonstrate that the sheaf projection and aggregation mechanisms significantly enhance predictive accuracy. This unified framework provides a more expressive and accurate representation of PPIs, advancing computational methods for drug discovery and biological network analysis.