Recent strategies and methodological advances for microbial natural product research in the post-genomics era

Microorganisms remain a prolific source of bioactive compounds, yet discovery efforts are often hindered by traditional methods and the repeated isolation of known molecules. In the post-genomics era, advances in genome mining and multi-omics technologies have revealed the hidden potential of biosynthetic gene clusters. A major challenge, however, lies in reliably linking these gene clusters to their metabolites and in the activation of silent pathways. This review summarizes recent breakthroughs addressing these challenges, focusing on the most recent case studies (up to 2025), and covering three interconnected themes. First, it reviews the current strengths and limitations of microbial genomics and artificial intelligence in natural product discovery, emphasizing different genome-guided discovery strategies and the emerging applications of artificial intelligence. Second, it highlights strategies for activating silent gene clusters, covering both untargeted and targeted approaches. Third, it discusses the expansion of chemical diversity through bioprospecting in underexplored ecological niches, the integration of sustainable, ethically informed practices, and the development of novel cultivation platforms. By synthesizing these advances, this review provides a forward-looking perspective, proposing how the integration of current tools in one framework can establish a predictive and potentially highly efficient method for linking biosynthetic gene clusters to their metabolites. Such integrative approaches may accelerate the discovery of microbial natural products and contribute sustainable solutions to global health challenges, including antimicrobial resistance. In conclusion, this review represents an up-to-date roadmap for researchers in microbial natural product research from a biological perspective, identifies existing strengths and knowledge gaps, and highlights promising, proof-of-concept strategies that can drive future advances in the field.