MSNovelist: de novo structure generation from mass spectra
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Abstract
Current methods for structure elucidation of small molecules rely on finding similarity with spectra of known compounds, but do not predict structures de novo for unknown compound classes. We present MSNovelist, which combines fingerprint prediction with an encoder–decoder neural network to generate structures de novo solely from tandem mass spectrometry (MS 2 ) spectra. In an evaluation with 3,863 MS 2 spectra from the Global Natural Product Social Molecular Networking site, MSNovelist predicted 25% of structures correctly on first rank, retrieved 45% of structures overall and reproduced 61% of correct database annotations, without having ever seen the structure in the training phase. Similarly, for the CASMI 2016 challenge, MSNovelist correctly predicted 26% and retrieved 57% of structures, recovering 64% of correct database annotations. Finally, we illustrate the application of MSNovelist in a bryophyte MS 2 dataset, in which de novo structure prediction substantially outscored the best database candidate for seven spectra. MSNovelist is ideally suited to complement library-based annotation in the case of poorly represented analyte classes and novel compounds.
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MSNovelist predicts structure de novo from fingerprints.
“MSNovelist168 takes these fingerprints a step further and predicts structure de novo.”
From Connecting metabolome and phenotype: recent advances in functional metabolomics tools for the identification of bioactive natural products (2024) · cites this paper as [168]
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- Authors
- Stravs, M. A., Dührkop, K., Böcker, S., & Zamboni, N.
- Affiliations
- ETH Zurich (CH)
- Friedrich Schiller University Jena (DE)
- Corresponding author
- Zamboni, Nicola
- Venue
- Nature Methods
- Published
- 2022-05-30
- Volume
- 19
- Issue
- 7
- Pages
- 865-870
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC9262714
- OpenAlex
- W4281688302
- Cited by
- 226
- References
- 48
- Citations by year
- 2026: 69 · 2025: 57 · 2024: 46 · 2023: 42 · 2022: 10 · 2021: 1 · 2020: 1
- FWCI
- 13.99
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Methods (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DORA Eawag (Swiss Federal Institute of Aquatic Science and Technology (Eawag)) (submittedVersion, other-oa)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by-nc-nd)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- ETH Zürich Research Collection
- Funders
- This project and NZ is supported by grants from the Strategic Focal Area Personalized Health and Related Technologies (PHRT) of the ETH Domain and by ETH Zürich.
- MS is supported by grants from the Strategic Focal Area Personalized Health and Related Technologies (PHRT) of the ETH Domain and by ETH Zürich.
- K.D. and S.B. are supported by Deutsche Forschungsgemeinschaft
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Keywords
- de novo structure prediction, tandem mass spectrometry, MS2 spectra, structure elucidation, natural products, encoder-decoder neural network, molecular networking, bryophytes, novel compound discovery
- MeSH terms
- Databases, Factual, Tandem Mass Spectrometry
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