Computational Prediction of Electron Ionization Mass Spectra to Assist in GC/MS Compound Identification
Bibliographic record
Abstract
We describe a tool, competitive fragmentation modeling for electron ionization (CFM-EI) that, given a chemical structure (e.g., in SMILES or InChI format), computationally predicts an electron ionization mass spectrum (EI-MS) (i.e., the type of mass spectrum commonly generated by gas chromatography mass spectrometry). The predicted spectra produced by this tool can be used for putative compound identification, complementing measured spectra in reference databases by expanding the range of compounds able to be considered when availability of measured spectra is limited. The tool extends CFM-ESI, a recently developed method for computational prediction of electrospray tandem mass spectra (ESI-MS/MS), but unlike CFM-ESI, CFM-EI can handle odd-electron ions and isotopes and incorporates an artificial neural network. Tests on EI-MS data from the NIST database demonstrate that CFM-EI is able to model fragmentation likelihoods in low-resolution EI-MS data, producing predicted spectra whose dot product scores are significantly better than full enumeration "bar-code" spectra. CFM-EI also outperformed previously reported results for MetFrag, MOLGEN-MS, and Mass Frontier on one compound identification task. It also outperformed MetFrag in a range of other compound identification tasks involving a much larger data set, containing both derivatized and nonderivatized compounds. While replicate EI-MS measurements of chemical standards are still a more accurate point of comparison, CFM-EI's predictions provide a much-needed alternative when no reference standard is available for measurement. CFM-EI is available at https://sourceforge.net/projects/cfm-id/ for download and http://cfmid.wishartlab.com as a web service.
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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) | 0.000 | 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".