AMP-UGen: Promoting Antimicrobial Peptide Generation through Peptide Understanding

Existing antimicrobial peptide (AMP) generation models primarily learn the probability distribution of known AMP sequences. Although this paradigm can produce diverse AMP-like candidates, generation is commonly driven by sequence likelihoods, property labels, or predictor scores, without explicitly incorporating the functional and physicochemical knowledge captured by peptide understanding. We introduce AMP-UGen, an understanding-driven generation framework that turns such knowledge into learnable signals through two complementary mechanisms, Understanding-to-Design Transfer (UDT) and Understanding-Feedback Policy Optimization (UFPO). UDT transfers design-semantic states extracted by the peptide understanding model to a masked diffusion model, allowing peptide understanding to guide sequence construction. UFPO reuses the same understanding component to evaluate generated candidates and converts its assessments into feedback for updating the generation policy. Among the evaluated generation models, AMP-UGen achieves the highest predicted-positive rates under the frozen HydrMIC and AMPlify predictors, reaching 56.72% and 82.34%, respectively. During property optimization, UFPO reduces charge MAE from 1.183 to 0.311 and hydrophobic-moment MAE from 0.0273 to 0.0163. These results show that AMP-UGen improves predicted AMP quality and property control by integrating peptide understanding into both sequence construction and policy optimization, demonstrating its potential as a general framework for peptide generation.