Enantioselective Carbonylative Coupling Reactions: Merging Nickel-Based Selectivity and Photoredox Reactivity
Bibliographic record
Abstract
Transition metal-catalyzed carbonylative coupling reactions play a crucial role in the synthesis of functional molecules of use throughout pharmaceutical development, natural products, and material science. This utility is driven by both the efficiency of carbonylation chemistry and the broad presence of the carbonyl functionality in most synthetic materials. Unfortunately, the development of enantioselective carbonylative coupling reactions of alkyl halides and nucleophiles to access the α-chiral motif found in most drugs is, to date, not viable. This has been attributed to the inhibitory influence of carbon monoxide, which blocks the activation of C(sp 3 )-halides and limits the efficacy of chiral ligand environments in modulating selectivity. Here, we show how this challenge can be addressed via a conceptually alternative approach to such reactions, where the coupling of photoredox and chiral nickel catalysis can be employed to separate reactivity from stereocontrol. This combined strategy has enabled the first asymmetric carbonylative coupling of benzylic and related C(sp 3 )-halides with amines and the preparation of a diverse array of chiral amides with excellent enantioselectivity. These findings expand the scope of enantioselective catalysis and offer new possibilities for synthesizing chiral carbonyl-containing compounds with wide-ranging implications for drug discovery and synthetic chemistry.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".