Regiochemical Tagging: A New Tool for Structural Characterization of Isomeric Components in Combinatorial Mixtures
Bibliographic record
Abstract
In this contribution we present a new combined synthetic and analytical strategy ( regiochemical tagging ) that allows facile determination of complete structure, including substituent position and regiochemistry, of mass-redundant components in complex combinatorial mixtures. The libraries of components (oxime ethers) are formed by the reaction of a mixture of substituents (aldehydes) with a scaffold containing several chemically similar attachment points (aminooxy groups). The structure of the resulting library components can then be determined from a combination of single MS and the tandem (MS/MS) spectra. Determination of the unique isomeric motif for each component is made possible via the following features of library design: (1) part of the scafforld moiety, “transferable group” (the nitrogen atom from the oxime group) is transferred to the substituent during fragmentation in the tandem experiment, (2) transferable groups on the scaffold differ from each other by either isotopic labels or fragmentation energies, and (3) mass-redundant substituents are isotopically labeled to create at least a 2 mass unit difference between them. The components of the resulting library thus become labeled with different mass- and energy tags, which allows for precise regiochemical assignment of the functional group positions on the scaffold and substituents by mass spectrometry. The approach has been used to create and analyze a mixture of 27 isomeric compounds, each containing three boronic acid groups. The combination of the MS and MS/MS spectra of the tagged mixture has yielded a unique and structurally definitive signature of each component. Applications of the regiochemical tagging techniques to rapid synthesis and screening of combinatorial mixtures are discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".