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 ROASMI model, which enables reliable prediction of retention order within a well-defined application domain by coupling data-driven molecular representation and mechanistic insights. The generalizability of ROASMI is proven by 71 independent reversed-phase liquid chromatography (RPLC) datasets. The application of ROASMI to four real-world datasets demonstrates its advantages in distinguishing coexisting isomers with similar fragmentation patterns and in annotating detection peaks without informative spect

SUBMITTER: Sun FY 

PROVIDER: S-EPMC11829455 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature