Catalytic Asymmetric Synthesis of Morpholines. Using Mechanistic Insights To Realize the Enantioselective Synthesis of Piperazines
Bibliographic record
Abstract
An efficient and practical catalytic approach for the enantioselective synthesis of 3-substituted morpholines through a tandem sequential one-pot reaction employing both hydroamination and asymmetric transfer hydrogenation reactions is described. Starting from ether-containing aminoalkyne substrates, a commercially available bis(amidate)bis(amido)Ti catalyst is utilized to yield a cyclic imine that is subsequently reduced using the Noyori–Ikariya catalyst, RuCl [( S,S )-Ts-DPEN] (η 6 - p -cymene), to afford chiral 3-substituted morpholines in good yield and enantiomeric excesses of >95%. A wide range of functional groups is tolerated. Substrate scope investigations suggest that hydrogen-bonding interactions between the oxygen in the backbone of the ether-containing substrate and the [( S,S )-Ts-DPEN] ligand of the Ru catalyst are crucial for obtaining high ee’s. This insight led to a mechanistic proposal that predicts the observed absolute stereochemistry. Most importantly, this mechanistic insight allowed for the extension of this strategy to include N as an alternative hydrogen bond acceptor that could be incorporated into the substrate. Thus, the catalytic, enantioselective synthesis of 3-substituted piperazines is also demonstrated.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".