Bioinspired transfer methylation enabled by a photoactive reagent
Bibliographic record
Abstract
Radical methylation ranks among the most important yet challenging transformations in chemistry and biology, which often involves small and unstable radical intermediates, such as the methyl radical, and results in low reactivity and poor selectivity. Herein, we report a photoactive, biomimetic reagent to address some facets of these challenges by leveraging a bulky and stabilised α-aminomethyl radical, which can offer enhanced control over radical generation and transfer. Our bioinspired transfer methylation protocol enables direct and selective C(sp2)–H methylation across a wide spectrum of heteroarenes, from simple scaffolds to complex drug molecules, including the thus far elusive C4-methylation of free quinolines. Mechanistic studies reveal that the unique α-aminomethyl radical intermediate undergoes an addition-elimination sequence reminiscent of natural methyltransferases and yields balanced reactivity and selectivity. Radical methylation ranks among the most important yet challenging transformations in chemistry and biology, which often involves small and unstable radical intermediates, and results in low reactivity and poor selectivity. Herein, the authors report a bioinspired transfer methylation protocol for the direct and selective C(sp2)-H methylation of heteroarenes.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".