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Despite progress in protein design, there are currently no robust approaches for de novo design of protein-binding macrocycles. Here we introduce RFpeptides, a denoising diffusion-based pipeline for designing macrocyclic binders against protein targets of interest. We tested 20 or fewer designed macrocycles against each of four diverse proteins and obtained binders with medium to high affinity against all targets. For one of the targets, Rhombotarget A (RbtA), we designed a high-affinity binder (\n                    <jats:italic>K<\/jats:italic>\n                    <jats:sub>d<\/jats:sub>\n                    \u2009&lt;\u200910\u2009nM) despite starting from the predicted target structure. X-ray structures for macrocycle-bound myeloid cell leukemia 1, \u03b3-aminobutyric acid type A receptor-associated protein and RbtA complexes match closely with the computational models, with a C\u03b1 root-mean-square deviation\u2009&lt;\u20091.5\u2009\u00c5 to the design models. RFpeptides provides a framework for rapid and custom design of macrocyclic peptides for diagnostic and therapeutic applications.\n                  <\/jats:p>","DOI":"10.1038\/s41589-025-01929-w","type":"journal-article","created":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T05:02:58Z","timestamp":1750395778000},"page":"1948-1956","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":101,"title":["Accurate de novo design of high-affinity protein-binding macrocycles using deep learning"],"prefix":"10.1038","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9797-6939","authenticated-orcid":false,"given":"Stephen 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