‘All‐in‐One’ analysis for metabolite identification using liquid chromatography/hybrid quadrupole time‐of‐flight mass spectrometry with collision energy switching
Bibliographic record
Abstract
The removal of bottlenecks in discovery stage metabolite identification studies is an ongoing challenge for the pharmaceutical industry. We describe the use of an 'All-in-One' approach to metabolite characterization that leverages the fast scanning and high mass accuracy of hybrid quadrupole time-of-flight mass spectrometry (QqToFMS) instruments. Full-scan MS and MS/MS data is acquired using collision energy switching without the preselection, either manually or in a data-dependent manner, of precursor ions. The acquisition of 'clean' MS/MS data is assisted by the use of ultrahigh-performance chromatography. Data acquired using this method can then be mined post-acquisition in a number of ways. These include using narrow window extracted ion chromatograms (nwXICs) for expected biotransformations, XICs for the product ions of the parent compound and/or expected modification of these product ions, and neutral loss chromatograms. This approach has the potential to be truly comprehensive for the determination of in vitro biotransformations in a drug discovery environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".