ReviewJournal of pharmacy & pharmaceutical sciences : a publication of the Canadian Society for Pharmaceutical Sciences, Societe canadienne des sciences pharmaceutiques2026
Artificial intelligence directed computational protein design: lessons from COVID-19 for pandemic-ready vaccines and antibody therapeutics.
Review in Journal of pharmacy & pharmaceutical sciences : a publication of the Canadian Society for Pharmaceutical Sciences, Societe canadienne des sciences pharmaceutiques, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Computational mapping of resistance-relevant signals in proteins using deep and classical feature spaces.Frontiers in microbiology · 2026Article
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Authors and funding
2 authors.
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Abstract
Artificial intelligence (AI) directed computational protein design has emerged as a transformative force in modern therapeutic discovery, reshaping how vaccines and antibody-based interventions are conceived, optimized, and deployed against emerging infectious diseases. The COVID-19 pandemic served as an unprecedented real-world stress test for these technologies, highlighting their potential to accelerate antigen design, guide antibody optimization, and anticipate viral evolution in near real time. AI driven approaches contributed to faster characterization of viral variants, supported vaccine and broadly neutralizing antibodies developments. Despite the significant contributions, the pandemic also revealed important limitations that must be addressed before such approaches can be relied upon as cornerstones of global preparedness. Challenges related to data bias, model interpretability, experimental validation bottlenecks, and integration with existing regulatory frameworks became increasingly apparent. In several cases, the gap between computational promise and translational readiness underscored the need for closer coupling between
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