摘要Functional peptides are short amino acid fragments that have a wide range of beneficial functions for living organisms.The majority of previous studies have focused on mono-functional peptides,but an increasing number of multi-functional peptides have been discovered.Although there have been enormous experimental efforts to assay multi-functional peptides,only a small portion of millions of known peptides has been ex-plored.The development of effective and accurate techniques for identifying multi-functional peptides can facilitate their discovery and mecha-nistic understanding.In this study,we presented iMFP-LG,a method for multi-functional peptide identification based on protein language mod-els(pLMs)and graph attention networks(GATs).Our comparative analyses demonstrated that iMFP-LG outperformed the state-of-the-art methods in identifying both multi-functional bioactive peptides and multi-functional therapeutic peptides.The interpretability of iMFP-LG was also illustrated by visualizing attention patterns in pLMs and GATs.Regarding the outstanding performance of iMFP-LG on the identification of multi-functional peptides,we employed iMFP-LG to screen novel peptides with both anti-microbial and anti-cancer functions from millions of known peptides in the UniRef90 database.As a result,eight candidate peptides were identified,among which one candidate was validated to process both anti-bacterial and anti-cancer properties through molecular structure alignment and biological experiments.We anticipate that iMFP-LG can assist in the discovery of multi-functional peptides and contribute to the advancement of peptide drug design.