Antimicrobial peptides: Bioinformatic advances and translational therapeutics to combat antibiotic resistance

Antimicrobial peptides (AMPs), also known as host defense peptides (HDPs), constitute a class of endogenous oligopeptides that are integral to the innate immune system. These peptides exhibit potent and broad-spectrum antimicrobial activity, targeting a diverse array of pathogens, including Gram-positive and Gram-negative bacteria, enveloped and non-enveloped viruses, fungi, and select protozoan species. In addition to their immunological relevance, AMPs are increasingly recognized as promising candidates for next-generation therapeutics, particularly in the context of the escalating antimicrobial resistance (AMR) crisis. Their multifaceted mechanisms of action and molecular specificity position them as key candidates for biomedical research and pharmaceutical advancement. This comprehensive review delineates the current landscape of AMP research, emphasizing recent advances in structural classification, mechanistic elucidation, and therapeutic development. Special emphasis is given to the proliferation of AMP-centric bioinformatics repositories and algorithmic platforms that leverage machine learning (ML) and deep learning (DL) architectures to enable high-throughput peptide mining, rational design, functional annotation, and optimization. The integration of computational modeling with experimental pipelines such as molecular docking, atomistic simulations, and free energy perturbation analyses has significantly enhanced the resolution of peptide-protein interaction studies, facilitating predictive modeling at sub-nanometer scales. Clinical translational barriers are critically assessed, with a focus on emergent strategies that enhance pharmacokinetic stability, target specificity, and immunocompatibility of AMP-based formulations. Moreover, the review explores frontier technologies, including quantum-enhanced computing, adaptive algorithmic evolution, and deterministic sampling paradigms that are redefining the computational efficiency and design fidelity of peptide engineering workflows. Overall, this review underscores the transformative impact of advanced technological innovations in accelerating peptide discovery and bridging the gap between laboratory research and clinical practice.