TCoCPIn reveals topological characteristics of chemical protein interaction networks for novel feature discovery.

Understanding chemical-protein interactions (CPIs) is crucial for drug discovery and biological research, yet their complexity often challenges traditional methods. We propose TCoCPIn, a novel framework integrating graph neural networks (GNN) with the comprehensive topological characteristics index (CTC), which combines multiple topological metrics to enhance predictive accuracy. TCoCPIn significantly outperforms traditional and embedding-based methods, achieving higher accuracy, precision, and recall in CPI prediction. A case study highlights its ability to predict potential interactions, such as between ibuprofen and TNF-alpha, demonstrating its utility in identifying novel therapeutic targets. These findings illustrate TCoCPIn's potential to uncover hidden associations and key nodes in CPI networks, providing new opportunities for drug discovery and disease mechanism exploration. Future work will focus on experimental validation and expanding the application of TCoCPIn to other biological systems.