Artificial Intelligence-Assisted Design and Optimization of AMP–Re(CO)3 (Nano)Microgel Systems: A New Perspective in Combating Antimicrobial Resistance

The escalating global threat of antimicrobial resistance (AMR) necessitates innovative therapeutic platforms beyond conventional antibiotics. Antimicrobial peptides (AMPs) coordinated with rhenium(I) tricarbonyl complexes ([fac-Re(CO)3]+) represent a promising class of metallodrugs, merging the rapid membrane-disrupting action of AMPs with the oxidative stress, intracellular targeting, or theranostic potential of Re(I) centers. Encapsulation of these conjugates into (nano)microgel matrices further improves proteolytic stability, enables controlled or stimuli-responsive release, and supports localized delivery while minimizing systemic toxicity. Artificial intelligence (AI), including deep learning, generative models, and multi-objective optimization, dramatically accelerates every stage—from de novo AMP sequence design and metal-binding motif engineering to Re coordination prediction and rational nanogel formulation. This review outlines a proposed framework in AI-driven AMP discovery, Re-metallopeptide synthesis, nanogel/microgel delivery systems, and their integrated application. It highlights machine-learning (ML) and generative approaches for rapid optimization while addressing key challenges such as data scarcity, scalability, biocompatibility, and in vivo validation. Future directions emphasize closed-loop, multi-objective AI frameworks for personalized, smart antimicrobial platforms.