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We’re sharing this article early to provide faster access to peer-reviewed, accepted research. It is citable and carries a permanent DOI. This version is subject to further edits and will be replaced automatically by the final Version of Record. All legal disclaimers apply. AbstractAntimicrobial lipopeptides (AMLPs) represent a promising solution to global antibiotic crisis, yet their precision design remains challenging due to the inability of current computational models to account for N-terminal fatty acid modification. Here we develop a deep learning-driven AMLP model for de novo design of AMLPs by integrating a chain-as-prompt mechanism with domain-adaptive transfer learning, effectively bridging the representation gap between peptide sequence and lipid-induced bioactivity. Using the AMLP model, 2657 evolutionarily selected RiPP-derived core peptide sequences from 96,911 bacterial genomes are inspiringly designed for virtual lipidation with seven N-terminal fatty acid chains (C6–C18) followed by predictive screening, generating 6197 candidate sequences.