Generating antimicrobial peptides via genomic transfer learning

Abstract

We present a generative machine learning pipeline for the design of linear antimicrobial peptides (AMPs). To extend diversity beyond synthetically validated peptide datasets ([~]7,000 entries), we apply transfer learning by training a Generative Pre-trained Transformer (GPT) on the genomically derived AMPSphere dataset ([~]863,000 entries), before fine-tuning on the Database of Antimicrobial Activity and Structure of Peptides (DBAASP). We assess the filtered sequences with a committee of Minimum Inhibitory Concentration (MIC) predictive models built with a Bi-LSTM architecture, and ESM-2 and QSAR feature vectors. The fine-tuned GPT model produced a 28% reduction in test loss compared to training on DBAASP alone, and generates peptides that are simultaneously more novel and more physicochemically plausible. Our top-ranked candidates are predicted to possess antimicrobial activity comparable to polymyxin B. We anticipate this transfer-learning approach is broadly applicable for leveraging massive, unlabelled genomic datasets to enrich targeted peptide discovery. Our identified sequences have been submitted to the 2027 AMP Challenge1 (team name VINCI) for experimental validation, and the complete codebase and workflow are open source2.