Mechanisms, Discovery Strategies, and Emerging Opportunities for AI-Assisted Design of Peptides in Cosmetics and Skin Health

Synthetic peptides are increasingly used in cosmetic products due to their potential to regulate biological processes related to skin aging, pigmentation, inflammation, extracellular matrix maintenance, and other aspects of skin health. However, the mechanisms of many cosmetic peptides are still not fully understood. Their activities can also be limited by poor stability, skin penetration, off-target effects, and limited clinical evidence. This review discusses the major classes of cosmetic peptides and the molecular mechanisms through which they are proposed to exert their activities. It also examines the main approaches used for peptide discovery. These include discovery from natural and biologically derived sources, library-based screening, rational design, and classical computational methods. More recent artificial intelligence (AI)-guided approaches are then discussed, with particular attention to structure-based and sequence-based peptide design. AI provides new opportunities to search much larger peptide sequence spaces and to prioritize candidates before experimental testing. Structure-based methods can use information about target binding sites to guide peptide design, while sequence-based approaches can identify interaction patterns without requiring detailed 3D structural information. These methods may also help improve peptide specificity, reduce potential off-target interactions, and identify naturally occurring peptides with useful properties. Although the application of AI to cosmetic peptide discovery is still at an early stage, it may provide a useful addition to existing approaches. Future progress will depend on better skin-relevant datasets, improved experimental validation, and design strategies that consider not only target binding, but also selectivity, stability, toxicity, delivery, and formulation compatibility.