Knowledge-Aware Graph Neural Networks for TRPV1 Drug-Gene Associations in Periodontal Pain

TRPV1 (Transient Receptor Potential Vanilloid 1) is a critical protein in the pathogenesis of periodontal pain, activated by noxious stimuli and inflammatory mediators associated with periodontitis. This study investigates drug-gene interactions involving TRPV1 to elucidate its role in periodontal pain mechanisms. Knowledge Graph Neural Networks (KGNNs) were employed to model and analyze the complex relationships between drugs, genes, and pain receptors in periodontal tissues. By leveraging biological datasets, including TRPV1 channel activity, pain receptor interactions, and gene expression profiles, the study aims to identify potential therapeutic targets and strategies for personalized pain management in periodontal treatment. Differentially expressed genes (DEGs) were integrated with drug and gene associations to model biological systems and inform therapeutic development. The study utilized a gene expression dataset encompassing features such as gene similarity scores, adjusted p-values, and biochemical activity. A semantic similarity-based fusion approach was applied to enhance model performance by incorporating biological information layers, improving interaction modeling, and promoting efficient information propagation. Three graph-based models were employed: Graph Convolutional Network (GCN) as a baseline, Residual GCN (ResGCN) for stability, and Attention-based GCN (AttGCN) for dynamic node weighting. Among the models, ResGCN demonstrated superior performance with an accuracy of 93.75% and the lowest final loss, highlighting its robustness in predicting drug-gene associations. This outcome supports the potential utility of ResGCN in accurately modeling TRPV1-mediated pain mechanisms and guiding therapeutic decisions. The application of KGNNs has provided valuable insights into TRPV1 drug-gene interactions in the context of periodontal pain. The findings emphasize the potential for using ResGCN in therapeutic discovery and optimization. However, challenges such as data quality and biological complexity remain.