AI-Assisted Discovery of Fungal Metabolites that Enhance Skin Barrier Function: Mechanisms and Therapeutic Potential

This review explores the emerging role of artificial intelligence in the identification and characterization of fungal metabolites with potential to reinforce the skin's barrier function. The primary aim is to review how machine learning algorithms and bioinformatics tools facilitate the discovery of bioactive compounds derived from the skin microbiome, particularly fungi, which influence skin homeostasis and integrity. The research methodology involves a comprehensive analysis of recent studies employing AI techniques such as network analysis, pattern recognition, and predictive modeling to screen fungal metabolites in silico. These approaches enable rapid identification of candidate molecules that modulate critical skin barrier components, including tight junction proteins, ceramide synthesis pathways, and antimicrobial peptides. The review summarizes key findings indicating that specific fungal metabolites can enhance barrier repair, reduce inflammation, and promote skin resilience. Moreover, it discusses the mechanistic insights gained through AI-driven prediction, such as targeting key molecular pathways involved in lipid metabolism, immune response, and microbial regulation. Finally, the paper highlights the therapeutic potential of these bioactives in dermatology, emphasizing the advantages of AI-assisted discovery in accelerating drug development for skin disorders related to barrier dysfunction. The review concludes with future perspectives on integrating AI, metabolomics, and microbiome research to advance personalized skincare solutions.

کلیدواژه ها:

Artificial intelligence ، Fungal metabolites ، Skin barrier function ، Mycobiome ، Metabolomics