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Here, we demonstrate a generalizable AI-driven method for designing protein fragments to control condensate formation, applying this approach across G3BP1, SARS-CoV-2 nucleocapsid, TDP-43, and focal adhesion kinase (FAK). Computationally screening 2,235 fragments, we selected 18 for experimental investigation, attaining a 50% success rate. Furthermore, predicted fragment binding modes align with their activities, revealing known and novel interactions driving condensate formation. For example, a fragment which suppresses FAK condensates in mammalian cells uncovered an interdomain interaction required for phase separation. Together, our results establish AI-guided protein fragment discovery as a generalizable strategy to dissect and control the molecular interactions that govern biomolecular condensates.<\/jats:p>","DOI":"10.64898\/2026.05.08.723928","type":"posted-content","created":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T05:05:14Z","timestamp":1778648714000},"source":"Crossref","is-referenced-by-count":2,"title":["AI-discovered protein fragments as generalizable regulators of biomolecular condensates"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0333-3199","authenticated-orcid":false,"given":"Andrew","family":"Savinov","sequence":"first","affiliation":[{"name":"Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, 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