Peptide Design and Optimization via Structured Full-Atom Editing

Peptide lead optimization seeks to improve molecular properties through selective modifications while retaining target engagement. The development of semaglutide from glucagon-like peptide-1 (GLP-1) illustrates this principle. Applying pocket-conditioned sequence–structure generation to lead optimization requires control over both which residues are modified and which molecular variables may change. Here we introduce EditFlow, a flow-matching model for structured full-atom editing of bound peptides. Residue-variable masks specify the scope of each edit, enabling a shared conditional model to regenerate selected amino acid identities, backbone geometry, and side-chain conformations while preserving the receptor and all unselected peptide variables. Coupled with external property evaluation and search, the same frozen editor supports iterative optimization through successive local modifications. Extensive evaluations on protein-peptide complexes demonstrate state-of-the-art performance in joint sequence-structure redesign alongside competitive full-generation performance. Across single- and multi-objective tasks, EditFlow outperforms strong baselines in improving molecular properties and achieving favorable trade-offs among competing objectives. These results establish structured full-atom editing as a unified approach to peptide design and lead optimization. The source code will be available upon acceptance.