Rapid Response Antimicrobial Peptide Design Strategy Driven by Meta-Learning for Emerging Drug-Resistant Pathogens

Antimicrobial resistance (AMR) presents a critical global health threat requiring urgent intervention. In order to swiftly respond to and control the spread of emerging drug-resistant bacteria at the onset of their proliferation, our aim is to develop a Rapid Response Antimicrobial Peptide (AMP) design strategy (RR-ADS). This framework addresses the challenge of limited pathogen-specific data by achieving robust generalization from minimal samples by meta-learning and reinforcement learning, optimizing both biocompatibility and efficacy against drug-resistant pathogens. Our model has achieved satisfactory results across multiple evaluation metrics, demonstrating the capability to accurately identify and generate AMPs targeted against drug-resistant bacteria with minimal sample sizes. Within 2 weeks, we successfully designed and experimentally verified AMPs against multidrug-resistant Acinetobacter baumannii, achieving a 93.3% positive rate. RR-ADS has effectively demonstrated the potential of meta-learning in tasks involving bioactive peptides and holds promise as an effective alternative measure to address infectious disease public health emergencies.