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<p>Integrated chem-bio characterization of microbial strain libraries can streamline natural product discovery by prioritizing candidate producers. Here, we employ language- and transformer-based models to extract actionable insights from linked mass spectrometry (MS)-genome datasets. Our framework enables ranking of microbial producers to prioritise high-potential candidates for targeted validation. Across three representative case studies, this approach prioritized producers of diverse natural products with 75–100% precision. These findings demonstrate the transformative potential of AI-enabled chem-bio characterization to significantly accelerate natural product discovery and enable access to microbial chemical diversity beyond reference knowledge.</p>

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Accelerating natural product discovery with linked MS-genomics and language/transformer-based models

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月30日

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  • Dillon W. P. Tay,
  • Winston Koh,
  • Shi Jun Ang,
  • Zicong Marvin Wong,
  • Yi Wee Lim,
  • Elena Heng,
  • Yu Hung Ng,
  • Naythan Z. X. Yeo,
  • Krishnan Adaikkappan,
  • Fong Tian Wong,
  • Yee Hwee Lim

摘要

Integrated chem-bio characterization of microbial strain libraries can streamline natural product discovery by prioritizing candidate producers. Here, we employ language- and transformer-based models to extract actionable insights from linked mass spectrometry (MS)-genome datasets. Our framework enables ranking of microbial producers to prioritise high-potential candidates for targeted validation. Across three representative case studies, this approach prioritized producers of diverse natural products with 75–100% precision. These findings demonstrate the transformative potential of AI-enabled chem-bio characterization to significantly accelerate natural product discovery and enable access to microbial chemical diversity beyond reference knowledge.