| Microbial natural products possess enormous diversity of chemical structures and fascinating biological activities, which continue to inspire novel discoveries in medicine and pesticide. In recent years, with the rapid development of high-throughput sequencing technology, massive microbial genomic data have revealed a much greater potential for the biosynthesis of diverse and novel natural products than previously appreciated. However, activating cryptic biosynthetic gene clusters (BGCs) and identifying the corresponding compounds, as well as avoiding rediscovery of known metabolites are still challenging. Here, this paper describes the new technologies such as genomics, bioinformatics, machine learning, metabolomics, gene editing and synthetic biology in the discovery of drug lead compounds to address the aforementioned problems. New insights into prioritizing promising strains, bioinformatics prediction of BGCs, efficient activation of silent BGCs, and identification and tracking of target products are summarized and presented. Finally, a systematic pipeline for efficient lead structure discovery from microbial natural products by promising strain selection and multi-omics mining (SPLSD) is established, and future opportunities and challenges in workflows of natural product drug lead discovery are discussed. |
