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Existing adaptations, such as imposing distance constraints via an offset matrix, enforce ring geometry but cannot specify the chemical identity of the cyclization bond, leading to a restrictive bias toward amide- or disulfide-linked macrocycles. Here, we present HighFold4, a framework that adapts AlphaFold3 to explicitly incorporate user-defined chemical connectivity between residues, thereby enabling both topological closure and bond-specific cyclization. Without retraining the base model, HighFold4 achieves accurate, chemistry-aware prediction of diverse cyclic peptide conformations, as validated on 179 structures. This work established a new paradigm for the conformation construction of macrocyclic peptides with tailored ring geometry and linkage chemistry, significantly expanding the utility of deep learning in peptide-based drug discovery.<\/jats:p>","DOI":"10.1093\/bib\/bbag505","type":"journal-article","created":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T11:19:43Z","timestamp":1788520783000},"source":"Crossref","is-referenced-by-count":1,"title":["HighFold4: extending AlphaFold3 to accurate cyclic peptide conformation prediction via custom chemical connectivity"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8499-5273","authenticated-orcid":false,"given":"Chengyun","family":"Zhang","sequence":"first","affiliation":[{"name":"Faculty of Applied Sciences, Macao Polytechnic University , R. de Lu\u00eds Gonzaga Gomes, Macao, 999078 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