Species- and Topic-aware Representation Learning for Antimicrobial Peptide Discovery

Antimicrobial resistance poses a major global health challenge, necessitating efficient strategies to discover potent antimicrobial peptides (AMPs). While recent generative models can produce many candidate sequences, experimentally validating all generated peptides in wet labs is impractical due to the high costs and time involved in such measurements. As a result, there is a strong demand for accurate predictions of peptide efficacy, typically measured as the minimum inhibitory concentration (MIC). We introduce STAMP, a framework for Species- and Topic-aware Representation Learning in AMP Discovery. This unified machine learning framework allows for cross-species predictions of AMP activity. STAMP integrates protein language model embeddings with species conditioning and topic-aware representations that capture sequence-level patterns, enabling generalizable predictions across multiple bacterial species within a single model. We evaluated STAMP on three benchmark datasets, which include two previously published datasets and a newly curated dataset derived from DBAASP, addressing duplicates and inconsistencies systematically. STAMP achieved strong predictive performance across these datasets, demonstrating a Pearson correlation coefficient (PCC) of 0.837 and an R ² of 0.70, outperforming several baseline models. Importantly, we further validated our prediction model using peptides that were experimentally tested for their antimicrobial activity against E . coli . and S . epidermidis bacteria, demonstrating its real-world applicability. Furthermore, residue-level importance analyses provide insights into the sequence determinants governing antimicrobial activity. Together, these results establish STAMP as a scalable framework for MIC prediction and an effective computational tool for accelerating AMP discovery and optimization.