VaxjoGNN: A Graph Neural Network for Ontology-Grounded Vaccine Adjuvant Recommendation

Abstract

Selecting an effective adjuvant remains a bottleneck in vaccine development, but most computational efforts have targeted antigen discovery rather than adjuvant prioritization. We frame disease-adjuvant matching as a top-k recommendation task on a heterogeneous knowledge graph grounded in biomedical ontologies, integrating curated facts, mechanistic pathways, and textual evidence. We introduce VaxjoGNN, a graph neural network trained with a listwise ranking objective. On a public benchmark, VaxjoGNN achieves NDCG@10 of 0.59 on seen diseases and 0.27 on previously unseen diseases (a 5.4x improvement over a random baseline). The framework provides an ontology-anchored approach to adjuvant prioritization that complements existing antigen-focused tools.