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<p>Microbial-derived secondary metabolites (SMs) hold great therapeutic potential but are predominantly discovered from cultured species, representing only a fraction of microbial biodiversity. Advances in metagenomics have unveiled reservoirs of biosynthetic gene clusters (BGCs), but translating genomic sequences into precise chemical structures remains challenging owing to the structural complexity of cryptic BGCs and the context-dependent substrate tolerance and cross-reactivity of modular biosynthetic domains. Here we present DeepSeMS, a transformer-based large language model that accurately predicts secondary metabolite chemical structures from BGC sequences. By encoding biosynthetic genes as functional domains and leveraging a feature-aligned data augmentation, DeepSeMS outperformed existing methods and successfully generated chemically valid predictions for 96.38% of cryptic BGCs. Applying DeepSeMS to a global ocean metagenome, we characterized over 60,000 secondary metabolites, revealing chemical diversity, ecological specificity and considerable biomedical potential, especially as antibiotics. This study underscores the capability of deep learning-driven approaches in revealing hidden biosynthetic potential of Earth’s largest, yet largely unexplored, microbial ecosystem.</p>

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DeepSeMS: revealing the hidden biosynthetic potential of the global ocean microbiome with a large language model

  • 期刊论文

  • 发表日期：

    2026年4月30日

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  • Tingjun Xu,
  • Yuwei Yang,
  • Ruixin Zhu,
  • Weili Lin,
  • Jixuan Li,
  • Yan Zheng,
  • Peng Zhang,
  • Guoqing Zhang,
  • Guoping Zhao,
  • Na Jiao

摘要

Microbial-derived secondary metabolites (SMs) hold great therapeutic potential but are predominantly discovered from cultured species, representing only a fraction of microbial biodiversity. Advances in metagenomics have unveiled reservoirs of biosynthetic gene clusters (BGCs), but translating genomic sequences into precise chemical structures remains challenging owing to the structural complexity of cryptic BGCs and the context-dependent substrate tolerance and cross-reactivity of modular biosynthetic domains. Here we present DeepSeMS, a transformer-based large language model that accurately predicts secondary metabolite chemical structures from BGC sequences. By encoding biosynthetic genes as functional domains and leveraging a feature-aligned data augmentation, DeepSeMS outperformed existing methods and successfully generated chemically valid predictions for 96.38% of cryptic BGCs. Applying DeepSeMS to a global ocean metagenome, we characterized over 60,000 secondary metabolites, revealing chemical diversity, ecological specificity and considerable biomedical potential, especially as antibiotics. This study underscores the capability of deep learning-driven approaches in revealing hidden biosynthetic potential of Earth’s largest, yet largely unexplored, microbial ecosystem.