Iterative Graph-Based Deep Learning for Modeling and Predicting Immunogenicity in Human Immune Response

Modeling molecular interactions such as HLA-peptide binding using graph neural networks (GNNs) is challenging due to incomplete and noisy domain knowledge that often leads to suboptimal heuristically constructed graph structures. To address this limitation, this research proposes an end-to-end graph learning framework, named GraphImmuno-IDEAL, which jointly refines graph topology and learns node embeddings. This approach leverages pairwise similarity learning and dynamic structure refinement to correct erroneous edges and discover meaningful interactions. Another new modelling technique, GraphImmuno-IDEALS introduces an anchor-based scalable graph learning mechanism to significantly reduce computational overhead while preserving performance. Experimental evaluation on multiple immunological datasets demonstrates that both latter models consistently outperform baseline classifiers and static GNNs in terms of accuracy and robustness. Furthermore, the learned task-specific graph structures enable interpretability through community detection and key interaction discovery, offering valuable insights for downstream applications in immunology and drug development.