Electrochemical deutero-(di)carboxylations for the preparation of deuterium-labeled medicinal building blocks
Bibliographic record
Abstract
Sacrificial anodes have been broadly deployed in electro-synthesis for the development of reductive electrosynthetic reactions. The metal cations released from sacrificial anodes during these processes are widely believed to not affect reaction outcomes. Here, we disclose an electrochemical deutero-(di)carboxylation of acetylenes and cinnamic acids that in fact relies on anodically generated Mg2+ cations to achieve regioselective α-carboxylation to afford deuterated malonic acids with precise control over both the site and amount of deuteration. The unusual, beneficial role of Mg2+ cations on product selectivity is supported by mechanistic studies and density functional theory [ZORA-B3LYP-D3BJ/def2-TZVP/DMF(SMD)] calculations, and is believed to mimic enzymatic α-carboxylation mechanisms. The deuteration patterns in the malonic acid products can be precisely controlled, providing a platform for the concise synthesis of high-value β-d₂- and β-d₁-α-amino acid analogs, as well as other precisely deuterated frameworks. The metal cations released from sacrificial anodes during reductive electrosynthetic reactions are widely believed to not affect reaction outcomes. Here, the authors disclose an electrochemical deutero-(di)carboxylation that relies on anodically generated Mg2+ cations to achieve selectivity in both the site and amount of deuteration.
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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 | 0.000 | 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) | 0.001 | 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 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".