Artificial Intelligence in the Discovery of Microbial Metabolites Relevant to Human Health: From Identification to Therapeutic Applications

Microbial metabolites represent a vast and diverse chemical repertoire with profound implications for human health, serving as key mediators in host-microbe interactions, immune regulation, and metabolic homeostasis. Despite their therapeutic promise, identifying bioactive molecules within the complex dark matter of the microbial metabolome remains a major bottleneck when relying solely on traditional, labor-intensive cultivation and chromatography methods. Recently, the integration of artificial intelligence (AI) and machine learning (ML) has emerged as a transformative paradigm to accelerate this discovery pipeline. AI-driven computational frameworks, such as deep learning neural networks, random forests, and molecular docking algorithms, enable high-throughput screening of biosynthetic gene clusters (BGCs) and automated annotation of mass spectrometry data. These computational approaches facilitate the rapid prediction of metabolite structures, biological targets, and pharmacokinetics. This review synthesizes current advancements in AI-assisted microbial metabolomics, examines the molecular mechanisms of newly discovered health-promoting metabolites, and addresses the computational challenges of clinical translation. By bridging microbiology with machine intelligence, this research highlights a shift toward AI-guided precision medicine and the accelerated development of next-generation biotherapeutics.

کلیدواژه ها:

Artificial Intelligence ، Microbial Metabolites ، Drug Discovery ، Machine Learning ، Biosynthetic Gene Clusters ، Precision Medicine