{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T09:45:46Z","timestamp":1791798346961,"version":"4.3.4"},"reference-count":267,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T00:00:00Z","timestamp":1791763200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T00:00:00Z","timestamp":1791763200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Biotech &amp; Bioengineering"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Global aquaculture faces an escalating disease and antimicrobial resistance (AMR) crisis, exacerbated by climate\u2010driven physiochemical shifts and intensive farming practices. The integration of artificial intelligence (AI) and structural biology, spearheaded by AlphaFold, represents a paradigm shift in aquatic pharmacology, transitioning the field from empirical trial\u2010and\u2010error to rational in silico design. This review critically evaluates the application of AI\u2010driven tools in aquaculture drug discovery and vaccine development. We examine the evolution of AlphaFold in predicting complex host\u2013pathogen interactions and facilitating de novo drug design. Concurrently, we highlight critical limitations stemming from the reliance on multiple sequence alignment (MSA) depth rather than mere PDB taxonomic composition, the unreliability of direct molecular docking due to apo conformations, and the dynamic nature of psychrophilic adaptations in aquatic environments. Furthermore, we assess next\u2010generation structural vaccinology, including reverse vaccinology and pre\u2010fusion antigen stabilization, alongside the integration of nanomaterial\u2010based delivery systems to overcome aquatic pharmacokinetic barriers. The synergy of AI with CRISPR\/Cas technologies and multimodal large language models (MM\u2010LLMs) for disease management is also explored. Despite unprecedented computational capabilities, significant bottlenecks remain, including a severe lack of species\u2010specific omics data, the indispensability of in vivo wet\u2010lab validation, and lagging regulatory frameworks. Ultimately, realizing the potential of precision aquatic medicine requires developing aquaculture\u2010specific databases, addressing the ecological risks of open\u2010water deployment, and democratizing these technologies for small\u2010scale farmers to ensure sustainable, equitable, and resilient global aquaculture.<\/jats:p>","DOI":"10.1002\/bit.70398","type":"journal-article","created":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T08:55:45Z","timestamp":1791795345000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From Sequence to Structure: AlphaFold and the AI Revolution in Aquaculture Drug Discovery and Vaccine Design"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3837-5049","authenticated-orcid":false,"given":"Enes","family":"\u00dcst\u00fcner","sequence":"first","affiliation":[{"name":"Department of Fisheries and Diseases, Faculty of Veterinary Medicine Aksaray University 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