Artificial Intelligence for Therapeutic Peptide Discovery and Development

Therapeutic peptides combine biological specificity with chemical versatility, and artificial intelligence (AI) now contributes to their identification, design, and optimisation. Peptide discovery, however, cannot be reduced to generating amino-acid sequences: therapeutic performance also depends on chemistry, conformation, assay context, stability, delivery, safety, manufacturability, and in vivo exposure. This critical review examines AI use across the major peptide classes: receptor agonists, protein-protein interaction inhibitors, oncology and tumour-targeting peptides, antimicrobial and phenotype-driven peptides, delivery peptides, vaccine epitopes, self-assembling materials, and chemically constrained macrocycles. It compares sequence-based, chemical, graph, structure-aware, and multimodal representations across predictive, generative, and experimentally integrated workflows. Coverage spans recent general protein foundation, prediction, and design systems (ProtGPT2, ESM-2/ESM3, ProGen/ProGen2, ProteinMPNN, RFdiffusion, and AlphaFold 3) and peptide-focused methods (RFpeptides, PepFlow, and PepGLAD). Particular attention is given to benchmark integrity, the limits of structure prediction for short and non-canonical molecules, and the gap between retrospective and prospective evidence. To make claims comparable, the review adapts established reporting, validation, and readiness concepts into a peptide-specific, two-dimensional Peptide-AI Evidence Grid that separates methodological rigour (M0-M3) from translational depth (T0-T3), applies it to representative studies, and provides a checklist extending DOME, TRIPOD + AI, and FAIR guidance with the chemistry- and assay-specific items peptide work requires.