Scalability of mass spectrometry-based metabolomics for natural extracts libraries exploration: current status, challenges, and opportunities

Scalability of mass spectrometry-based metabolomics for natural extracts libraries exploration: current status, challenges, and opportunities. Rutz, A., Bittremieux, W., Schmid, R., Cailloux, O., van der Hooft, J. J. J., & Beniddir, M. A. Natural Product Reports, The Royal Society of Chemistry, 2025.
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This review explores the potential of bioinformatics and chemoinformatics tools to advance the exploration of natural extracts libraries (NELs). Although metabolomics has become a term used routinely in natural product (NP) research, the field remains focused on individual molecules or small sets of compounds, which restricts scalability. This narrow focus is mirrored in the computational handling of generated data, limiting broader insights. By challenging the traditional molecule-first paradigm—a framework historically shaped by practical constraints—we present our vision of using computational approaches to unlock the full potential of NELs, now and in the future.

@article{rutz_scalability_2025,
author ="Rutz, Adriano and Bittremieux, Wout and Schmid, Robin and Cailloux, Olivier and van der Hooft, Justin J. J. and Beniddir, Mehdi A.",
title  ="Scalability of mass spectrometry-based metabolomics for natural extracts libraries exploration: current status{,} challenges{,} and opportunities",
journal  ="Natural Product Reports",
year  ="2025",
publisher  ="The Royal Society of Chemistry",
doi  ="10.1039/D5NP00034C",
abstract  ="This review explores the potential of bioinformatics and chemoinformatics tools to advance the exploration of natural extracts libraries (NELs). Although metabolomics has become a term used routinely in natural product (NP) research{,} the field remains focused on individual molecules or small sets of compounds{,} which restricts scalability. This narrow focus is mirrored in the computational handling of generated data{,} limiting broader insights. By challenging the traditional molecule-first paradigm—a framework historically shaped by practical constraints—we present our vision of using computational approaches to unlock the full potential of NELs{,} now and in the future."
}

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