MyCompoundID MS/MS Search: Metabolite Identification Using a Library of Predicted Fragment-Ion-Spectra of 383,830 Possible Human Metabolites
Bibliographic record
Abstract
We report an analytical tool to facilitate metabolite identification based on an MS/MS spectral match of an unknown to a library of predicted MS/MS spectra of possible human metabolites. To construct the spectral library, the known endogenous human metabolites in the Human Metabolome Database (HMDB) (8,021 metabolites) and their predicted metabolic products via one metabolic reaction in the Evidence-based Metabolome Library (EML) (375,809 predicted metabolites) were subjected to in silico fragmentation to produce the predicted MS/MS spectra. This spectral library is hosted at the public MCID Web site ( www.MyCompoundID.org ), and a spectral search program, MCID MS/MS, has been developed to allow a user to search one or a batch of experimental MS/MS spectra against the library spectra for possible match(s). Using MS/MS spectra generated from standard metabolites and a human urine sample, we demonstrate that this tool is very useful for putative metabolite identification. It allows a user to narrow down many possible structures initially found by using an accurate mass search of an unknown metabolite to only one or a few candidates, thereby saving time and effort in selecting or synthesizing metabolite standard(s) for eventual positive metabolite identification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".