{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T16:05:49Z","timestamp":1787241949208,"version":"build-2736575974"},"reference-count":45,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:00:00Z","timestamp":1778198400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontierspartnerships.org"],"crossmark-restriction":true},"short-container-title":["J. Pharm. Pharm. Sci."],"abstract":"<jats:p>\n                    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\n                    <jats:italic>in silico<\/jats:italic>\n                    design, laboratory experimentation, and clinical evaluation. Moreover, the rapid pace of AI innovation often outstripped established regulatory pathways, raising questions about standardization, validation, and long-term safety. This mini review provides a focused overview of recent advances in AI enabled computational protein design, with an emphasis on applications relevant to pandemic response. Drawing on lessons from COVID-19 case studies, it examines translational and regulatory considerations, highlights unresolved controversies, and identifies critical research gaps. Collectively, these insights outline a path toward transitioning AI designed vaccines and antibody therapeutics from reactive emergency tools into proactive, scalable infrastructures for future pandemic preparedness.\n                  <\/jats:p>","DOI":"10.3389\/jpps.2026.16146","type":"journal-article","created":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T05:10:17Z","timestamp":1778217017000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Artificial intelligence directed computational protein design: lessons from COVID-19 for pandemic-ready vaccines and antibody therapeutics"],"prefix":"10.3389","volume":"29","author":[{"given":"Rahul","family":"Kaushik","sequence":"first","affiliation":[{"name":"Laboratory of In-Silico Design, Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition (NIBN)","place":["Osaka, Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suyong","family":"Re","sequence":"additional","affiliation":[{"name":"Laboratory of In-Silico Design, Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition (NIBN)","place":["Osaka, Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,5,8]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1038\/s41586-023-06617-0","article-title":"Learning from prepandemic data to forecast viral escape","volume":"622","author":"Thadani","year":"2023","journal-title":"Nature"},{"key":"B2","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1126\/science.abd7331","article-title":"Learning the language of viral evolution and 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