Differentiation of structural isomers in a target drug database by LC/Q‐TOFMS using fragmentation prediction
Bibliographic record
Abstract
Isomers cannot be differentiated from each other solely based on accurate mass measurement of the compound. A liquid chromatography/quadrupole time-of-flight mass spectrometry (LC/Q-TOFMS) method was used to systematically fragment a large group of different isomers. Two software programs were used to characterize in silico mass fragmentation of compounds in order to identify characteristic fragments. The software programs employed were ACD/MS Fragmenter (ACD Labs Toronto, Canada), which uses general fragmentation rules to generate fragments based on the structure of a compound, and SmartFormula3D (Bruker Daltonics), which assigns fragments from a mass spectra and calculates the molecular formulae for the ions using accurate mass data. From an in-house toxicology database of 874 drug substances, 48 isomer groups comprising 111 compounds, for which a reference standard was available, were found. The product ion spectra were processed with the two software programs and 1-3 fragments were identified for each compound. In 82% of the cases, the fragment could be identified with both software programs. Only 10 isomer pairs could not be differentiated from each other based on their fragments. These compounds were either diastereomers or position isomers undergoing identical fragmentation. Accurate mass data could be utilized with both software programs for structural elucidation of the fragments. Mean mass accuracy and isotopic pattern match values (SigmaFit; Bruker Daltonics Bremen, Germany) were 0.9 mDa and 24.6 mSigma, respectively. The study introduces a practical approach for preliminary compound identification in a large target database by LC/Q-TOFMS without necessarily possessing reference standards.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".