ArticleMetabolomics : Official journal of the Metabolomic Society2026
From known chemical space to unannotated metabolites: a cluster-guided retention-time driven framework for biologically informed annotation.
Article in Metabolomics : Official journal of the Metabolomic Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
introductionUntargeted metabolomics often results in a significant portion of unannotated metabolites, or "metabolic dark matter," which hinders biological interpretation.
objectivesA two-step analytical approach was developed to systematically prioritize and interpret unannotated metabolites using plasma LC-MS/MS data from pregnant women with obesity as a biologically relevant test dataset.
methodsThe first step involved clustering 1,021 known metabolites into ten structurally coherent groups based on the Tanimoto similarity, thus defining the biologically relevant chemical space of the dataset. These metabolites were further characterized by Absorption, Distribution, Metabolism, and Excretion (ADME) profiling, protein target prediction, molecular docking and Kyoto Encyclopedia of Genes and Genomes pathway mapping analysis, to establish biological plausibility and functional perspective. Candidate structures for 1,836 unannotated features were retrieved from PubChem using molecular formula and molecular weight matching within a ±0.5 Da tolerance.
resultsThis search yielded 569,115 candidate structures, of which 368,197 unique structures were retained after curation. Tanimoto coefficient filtering reduced the candidate pool to 19,868 structurally plausible candidates, and retention time-based prioritization further refined this set to 418 high confidence candidate annotations, including 83 database-supported candidates identified through HMDB and LIPID MAPS structure database cross-referencing. RT-based prioritization effectively distinguished positional isomers sharing the same molecular formula by incorporating agreement between predicted and experimentally observed retention times.
conclusionThis improved discrimination among structurally similar candidates, expanded metabolite annotation confidence, and provided a scalable framework for prioritizing dark matter metabolites in untargeted metabolomics.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.