AMPLE: A Cost-Aware Adaptive Multi-Expert Framework for Antimicrobial Peptide Prediction

Antimicrobial peptides (AMPs) are key components of innate immune defense and promising candidates for combating antimicrobial resistance, making accurate prediction of their diverse antibacterial, antifungal, antiviral, and antiparasitic activities important for identifying broad-spectrum anti-infective peptides. However, existing AMP prediction methods often rely on limited information sources or fixed strategies for integrating heterogeneous representations, limiting their ability to capture the distinct predictive characteristics of different types of biological information. To address this issue, we propose AMPLE, a cost-aware adaptive multi-expert learning framework for antimicrobial peptide activity prediction that models heterogeneous biological representations through specialized experts and selectively integrates their predictive capabilities within a unified architecture. AMPLE achieves state-of-the-art performance on the evaluated benchmark while substantially reducing unnecessary computation from expensive experts, demonstrating the effectiveness of adaptive expert acquisition for multilabel AMP activity prediction. Its design enables flexible performance–cost trade-offs across different computational budgets, providing a general framework for studying and modeling heterogeneous information in antimicrobial peptide prediction.