MS2Prop: A machine learning model that directly generates de novo predictions of drug-likeness of natural products from unannotated MS/MS spectra
Open accessAbstract
Mass spectrometry (MS) is a fundamental analytical tool for the study of complex molecular mixtures and in natural products drug discovery and metabolomics specifically, due to its high sensitivity, specificity, and throughput. A major challenge, however, is the lack of structurally annotated mass spectra for these applications. This deficiency is particularly acute for analyses conducted on extracts or fractions that are largely chemically undefined. This work describes the use of mass spectral data in a fundamentally different manner than structure determination; to predict properties or activities of structurally unknown compounds without the need for defined or deduced chemical structure using a machine learning (ML) model, MS2Prop. The model’s predictive accuracy and scalability is benchmarked against commonly used methods and its performance demonstrated in a natural products drug discovery setting. A new cheminformatic subdiscipline, quantitative spectra-activity relationships (QSpAR), using spectra rather than chemical structure as input, is proposed to describe this approach and to distinguish it from structure based quantitative methods.
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MS2Prop uses machine learning to predict chemical properties directly from MS/MS, avoiding structure prediction.
“An alternative approach, MS2Prop,169 aims to avoid the intermediate step of structure prediction, and rather uses machine learning to predict chemical properties directly from the MS/MS, e.g., drug likeness, synthetic accessibility, etc.”
From Connecting metabolome and phenotype: recent advances in functional metabolomics tools for the identification of bioactive natural products (2024) · cites this paper as [169]
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- Authors
- Voronov, G., Lightheart, R., Frandsen, A., Bargh, B., Haynes, S. E., Spencer, E., Schoenhardt, K. E., Davidson, C., Schaum, A., Macherla, V. R., DeBloois, E., Healey, D., Kind, T., Dorrestein, P., Colluru, V., Butler, T., & Yu, M. S.
- Affiliations
- University of California San Diego (US)
- Corresponding author
- Butler, Thomas
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2022-10-10
- OpenAlex
- W4304092658
- Cited by
- 18
- References
- 64
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 5 · 2023: 6
- Type
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- Language
- en
- Open access
- green
- doi.org
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- cc-by-nc-nd
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Keywords
- natural products, structure-activity relationship, natural product drug discovery, metabolomics, machine learning models
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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