Origin of enantioselectivity reversal in Lewis acid-catalysed Michael additions relying on the same chiral source
Bibliographic record
Abstract
Enantiodivergence is an important concept in asymmetric catalysis that enables access to both enantiomers of a product relying on the same chiral source as reagent. This strategy is particularly appealing as an alternate approach when only one enantiomer of the required chiral ligand is readily accessible but both enantiomers of the product are desired. Despite the potential significance, general catalytic methods to effectively reverse enantioselectivity by changing an achiral reaction parameter remain underdeveloped. Herein we report our studies focused on elucidating the origin of metal-controlled enantioselectivity reversal in Lewis acid-catalysed Michael additions. Rigorous experimental and computational investigations reveal that specific Lewis and Brønsted acid interactions between the substrate and ligand change depending on the ionic radius of the metal catalyst, and are key factors responsible for the observed enantiodivergence. This holds potential to further our understanding of and facilitate the design of future enantiodivergent transformations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".