AMP-Modify: Chemical Modification Design of Antimicrobial Peptides with a Closed-Loop Framework

Antimicrobial peptides (AMPs) offer a promising route to combating drug-resistant infections, but their therapeutic development requires addressing limitations in stability, selectivity, and activity. Chemical modifications, such as lipidation, cyclization, and terminal capping, provide a critical way to tune these properties beyond amino acid sequence optimization. However, computational AMP design has largely focused on sequence generation, leaving chemical modification underexplored. We formulate the modification problem as a joint decision over whether to modify a parent peptide, where, and what chemical structure to introduce. A central challenge is the mismatch between abundant general chemical knowledge and scarce experimental data linking parent peptides, modifications, and outcomes. Here, we develop AMP-Modify to address it by separating chemically explicit construction and validation from AMP-specific effect prediction. An LLM-assisted search uses general chemical knowledge to propose edit strategies within a modification space defined by a rule registry, while a molecular compiler instantiates and validates candidates. An AMP-specific modification evaluator trained on $8,145$ parent-modified relations scores each candidate relative to its parent, and its predictions are fed back to guide subsequent search and final selection. The evaluator outperforms existing methods in ranking modification effects, while case studies show that modifications proposed by our framework align with experimentally observed trends. AMP-Modify extends computational AMP design beyond sequence generation to chemically explicit modification.