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bioRxiv · 10.1101/2025.11.27.690985

VaxjoGNN: A Graph Neural Network forOntology-Grounded Vaccine Adjuvant Recommendation

He, Y.·Zheng, Y.
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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.

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He, Y., Zheng, Y.. 2025-12-01. VaxjoGNN: A Graph Neural Network forOntology-Grounded Vaccine Adjuvant Recommendation. https://doi.org/10.1101/2025.11.27.690985

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