Cycle-MS: A Closed-Loop End-to-End Framework for Mass Spectrometry Structure Elucidation

Mass spectrometry (MS) is indispensable for molecular analysis; however, elucidating complex molecular structures from spectra remains a major challenge. Here, we present Cycle-MS, an end-to-end framework that unifies inverse spectrum-to-molecule prediction with forward spectrum reconstruction, with cycle-consistency losses refining latent representations. Cycle-MS demonstrates enhanced accuracy in predicting atomic compositions, molecular formulas, scaffolds, and functional groups, offering robust recovery of molecular scale and key chemical features. Interpretability analysis shows that Cycle-MS prioritizes structurally diagnostic fragments over nonspecific abundant peaks, ensuring chemical plausibility and structural reliability. Together, these advances establish Cycle-MS as a robust, accurate, and interpretable framework that offers a path toward fully automated, high-precision MS-based structure elucidation.

Authors

Institutions

Publication Details

Journal
Journal of Chemical Information and Modeling
Published
2026-02-25
DOI
https://doi.org/10.1021/acs.jcim.5c02981
Primary Topic
Mass Spectrometry Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Cycle-MS: A Closed-Loop End-to-End Framework for Mass Spectrometry Structure Elucidation

Jinglong Lin, Hui Li, Fanyang Mo, Zhiyuan Yan et al.
Journal of Chemical Information and Modeling
Mass Spectrometry Techniques and Applications
article

Cycle-MS: A Closed-Loop End-to-End Framework for Mass Spectrometry Structure Elucidation

Jinglong Lin, Hui Li, Fanyang Mo, Zhiyuan Yan, Chengchun Liu, Li Yuan
article en

Abstract

Mass spectrometry (MS) is indispensable for molecular analysis; however, elucidating complex molecular structures from spectra remains a major challenge. Here, we present Cycle-MS, an end-to-end framework that unifies inverse spectrum-to-molecule prediction with forward spectrum reconstruction, with cycle-consistency losses refining latent representations. Cycle-MS demonstrates enhanced accuracy in predicting atomic compositions, molecular formulas, scaffolds, and functional groups, offering robust recovery of molecular scale and key chemical features. Interpretability analysis shows that Cycle-MS prioritizes structurally diagnostic fragments over nonspecific abundant peaks, ensuring chemical plausibility and structural reliability. Together, these advances establish Cycle-MS as a robust, accurate, and interpretable framework that offers a path toward fully automated, high-precision MS-based structure elucidation.

Journal of Chemical Information and ModelingVol. 66(5)
Peking University (CN), Peking University Shenzhen Hospital (CN), Preferred Networks (Japan) (JP)
Openalex Percentile: Top 44%
Mass Spectrometry Techniques and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.