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Related Experiment Video

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ROASMI: accelerating small molecule identification by repurposing retention data.

Fang-Yuan Sun1, Ying-Hao Yin1,2, Hui-Jun Liu1

  • 1State Key Laboratory of Natural Medicines, Department of Chinese Medicines Analysis, School of Traditional Chinese Pharmacy, China Pharmaceutical University, No. 24 Tongjia Lane, Nanjing, 210009, China.

Journal of Cheminformatics
|February 14, 2025
PubMed
Summary
This summary is machine-generated.

The ROASMI model enhances small molecule identification in untargeted metabolomics by reliably predicting retention order. This approach improves data replicability and aids in distinguishing isomers and annotating unknown compounds.

Keywords:
Deep learningMetabolomicsReplicabilityRetention orderSmall-molecule identification

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Area of Science:

  • Analytical Chemistry
  • Metabolomics
  • Computational Chemistry

Background:

  • Limited replicability of retention data in untargeted metabolomics hinders small molecule identification.
  • Existing retention order models lack generalizability and predictive reliability.

Purpose of the Study:

  • To present the ROASMI model for reliable prediction of retention order in reversed-phase liquid chromatography (RPLC).
  • To improve small molecule identification in untargeted metabolomics by enhancing data reproducibility.

Main Methods:

  • Coupling data-driven molecular representation with mechanistic insights to develop the ROASMI model.
  • Validating ROASMI generalizability across 71 independent RPLC datasets.
  • Applying ROASMI to real-world datasets for isomer differentiation and peak annotation.

Main Results:

  • ROASMI demonstrates proven generalizability across diverse RPLC datasets.
  • The model effectively distinguishes coexisting isomers with similar fragmentation patterns.
  • ROASMI aids in annotating detection peaks lacking informative spectra.

Conclusions:

  • ROASMI enables reliable retention order prediction, addressing key limitations in metabolomics.
  • The model's flexibility allows retraining and compatibility with other MS/MS scorers for improved small molecule identification.