AI-Assisted Identification of Bacterial Metabolites Enhancing the Skin Barrier: From Mechanisms to Therapeutic Applications

The skin barrier is a complex multi-layered system essential for maintaining homeostasis and protecting the body against environmental pathogens, allergens, and physical stressors. Recent scientific paradigms have shifted toward understanding the skin-microbiome axis, highlighting the critical role of bacterial metabolites, often referred to as postbiotics, in reinforcing the physical, chemical, and immunological integrity of the skin. Despite their therapeutic potential, the identification of specific metabolites from the vast chemical space of the skin microbiota remains a significant challenge using traditional experimental methods. Emerging advancements in artificial intelligence (AI) and machine learning (ML) offer transformative opportunities to accelerate this discovery process. AI-driven models can integrate high-throughput metabolomics, genomics, and clinical data to predict the bioactivity of bacterial secretomes, identify novel barrier-enhancing compounds, and decode the complex molecular interactions at the skin-environment interface. Deep learning algorithms and predictive frameworks enable the screening of vast metabolite libraries with unprecedented speed and accuracy, facilitating the discovery of molecules that modulate tight junction proteins, regulate inflammatory cytokines, and promote wound healing. This review synthesizes current knowledge on AI-assisted discovery of bacterial metabolites, evaluates the molecular mechanisms of their barrier-reinforcing properties, and discusses the challenges of translating these computational findings into clinical dermatological therapies. By converging microbiology with computational intelligence, this research highlights a shift toward precision dermatology and the development of next-generation, metabolite-based skincare and therapeutic interventions.

کلیدواژه ها:

Skin Barrier ، Bacterial Metabolites ، Artificial Intelligence ، Machine Learning ، Microbiome-Skin Axis ، Postbiotics