ROASMI: accelerating small molecule identification by repurposing retention data

ROASMI: accelerating small molecule identification by repurposing retention data

Abstract The limited replicability of retention data hinders its application in untargeted metabolomics for small molecule identification. While retention order models hold promise in addressing this issue, their predictive reliability is limited by uncertain generalizability. Here, we present the R...

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Bibliographic Details
Main Authors: Fang-Yuan Sun, Ying-Hao Yin, Hui-Jun Liu, Lu-Na Shen, Xiu-Lin Kang, Gui-Zhong Xin, Li-Fang Liu, Jia-Yi Zheng
Format: Article
Language:English
Published: BMC 2025-02-01
Series:Journal of Cheminformatics
Subjects:
Metabolomics
Retention order
Small-molecule identification
Replicability
Deep learning
Online Access:https://doi.org/10.1186/s13321-025-00968-8
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https://doi.org/10.1186/s13321-025-00968-8

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