Comparative Performance Evaluation of GCN, GAT, and GraphSAGE Architectures for Drug-Gene Interaction Prediction in Ameloblastoma via MEK-Pathway Targeting

The findings indicate that each architecture exhibits distinct advantages: GraphSAGE demonstrates superior generalization in dynamic graph environments; GAT enables more nuanced modeling through attention mechanisms; and GCN remains computationally stable and efficient. These results provide biomedical informatics researchers with valuable insights to guide the selection of GNN architectures for biological graph learning tasks. To enhance the translational potential of GNN-based drug discovery pipelines, future research should focus on integrating dynamic graph structures, richer node features, and supervised learning approaches aligned with empirical biological outcomes.