ReviewFrontiers in immunology2026
Could artificial intelligence gradually replace classical adjuvants?
Review in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Adjuvants have been indispensable in vaccinology, enhancing immunogenicity, shaping adaptive immune polarization, and extending the durability of protective responses. Classical adjuvants, including alum, oil-in-water emulsions, liposomal formulations, and toll-like receptor agonists, function by fueling innate immunity, promoting antigen presentation, and modulating cytokine milieus. Yet, these compounds face persistent limitations such as reactogenicity, species-specific responses, manufacturing complexity, and regulatory barriers. Artificial intelligence (AI) and core subfields such as machine learning are revolutionizing vaccine design by enhancing antigen engineering, delivery system optimization, immunogenicity modeling, and
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.