MultiPepDec: Decoupled Prompt Learning for Multi-Activity Therapeutic Peptides

Therapeutic peptides demonstrate significant potential in anti-infection, antitumor, and immunomodulation therapies owing to their high specificity and low toxicity. However, existing computational methods are predominantly limited to single-activity design, restricting their clinical applicability. Here, we present MultiPepDec, a novel decoupled prompt learning framework based on protein language model for concurrent generation of multifunctional peptides, which includes antimicrobial, anticancer, toxic, and metabolic activities. Our approach employs: i) Shared-prompts capturing universal therapeutic patterns via adversarial purification; ii) Private-prompts encoding activity-specific knowledge through contrastive learning, ensuring functional decoupling between four activities. Experimental results demonstrate that generated antimicrobial peptides achieve 80.38% predicted efficacy against E. coli, with comparable performance against most clinically relevant pathogens. This confirms robust broad-spectrum capabilities without requiring pathogen-specific training, while maintaining low computational costs. For other therapeutic activities, the designed sequences not only exhibit the intended biological functions but also show significantly improved diversity. This work establishes a new paradigm for efficient multi-activity peptide design, with potential extensions to other biomolecular engineering domains.