Tay, D.W.P., Koh, W., Ang, S.J. et al. Accelerating natural product discovery with linked MS-genomics and language/transformer-based models. npj Antimicrob Resist 4, 31 (2026). https://doi.org/10.1038/s44259-026-00206-7
Abstract:
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.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
This research / project is supported by the National Research Foundation, Singapore - Competitive Research Programme
Grant Reference no. : NRF-CRP19-2017-05-0
This research / project is supported by the Agency for Science, Technology and Research (A*STAR), Singapore - Strategic Research Programme
Grant Reference no. : C211917003, C211917006, C233017006
This research is supported by core funding from: Singapore Integrative Biosystems and Engineering Research Strategic Research & Translational Thrust (SIBER SRTT, A*STAR)
Grant Reference no. : NA