CFlowAMP: Property controllable De Novo AMP design via integrating ESM-2 with conditional flow matching

The escalation of antimicrobial resistance (AMR) has become a major global public health threat, creating an urgent need for novel antimicrobial therapeutics. Antimicrobial peptides (AMPs), owing to their unique mechanisms of action and low propensity for inducing resistance, are regarded as key candidates for combating AMR. However, existing generative models struggle to balance generation quality, efficiency, and controllability. This study proposes the CFlowAMP framework, which integrates the protein language model ESM-2 with property-controlled Conditional Flow Matching to model a direct mapping trajectory from random noise to the latent representations of peptide sequences. Experimental results demonstrate that CFlowAMP substantially enhances generative performance: it increases the AMP generation success score by 39.8%, achieves an 18-fold speedup over diffusion-based models, and enables on-demand generation with controllable physicochemical properties. This approach provides a generalizable computational framework for therapeutic peptide design.