Catalytic Enantioselective Alkylation of Prochiral Enolates
Bibliographic record
Abstract
The asymmetric alkylation of enolates is a particularly versatile method for the construction of α-stereogenic carbonyl motifs, which are ubiquitous in synthetic chemistry. Over the past several decades, the focus has shifted to the development of new catalytic methods that depart from classical stoichiometric stereoinduction strategies (e.g., chiral auxiliaries, chiral alkali metal amide bases, chiral electrophiles, etc.). In this way, the enantioselective alkylation of prochiral enolates greatly improves the step- and redox-economy of this process, in addition to enhancing the scope and selectivity of these reactions. In this review, we summarize the origin and advancement of catalytic enantioselective enolate alkylation methods, with a directed emphasis on the union of prochiral nucleophiles with carbon-centered electrophiles for the construction of α-stereogenic carbonyl derivatives. Hence, the transformative developments for each distinct class of nucleophile (e.g., ketone enolates, ester enolates, amide enolates, etc.) are presented in a modular format to highlight the state-of-the-art methods and current limitations in each area.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".