Catalytic enantioselective transformations of borylated substrates: Preparation and synthetic applications of chiral alkylboronates
Bibliographic record
Abstract
Organoboronic acid derivatives are well-established intermediates for the preparation of alcohols and amines, and in the formation of C–C bonds via different reactions, including homologations, carbonyl allylboration, or transition-metal-catalyzed cross-coupling chemistry. In the past decade, there has been great interest in the development of catalytic enantioselective methods for the preparation of chiral, optically enriched organoboronates as precursors of enantioenriched compounds. While the mainstream strategy remains the late-stage borylation of organic functional groups, our group has focused on an alternate strategy focused on modification of boron-containing substrates. In this way, acyclic and cyclic secondary alkyl- and allyl-boronates were prepared through catalytic enantioselective processes such as [4 + 2] cycloadditions, isomerizations, allylic substitutions, and conjugate additions. The resulting optically enriched boronates have been successfully utilized in the syntheses of complex natural products and drugs. One remaining challenge in the chemistry of secondary alkylboronate derivatives is their cross-coupling, especially with control of stereoselectivity. In this regard, our recent approach featured the conjugate asymmetric borylation of β-boronyl acrylates, providing the first enantioselective preparation of highly optically enriched 1,1-diboronyl derivatives. The chirality of these geminal diboron compounds is conferred through the use of two distinct boronate adducts, which can be coupled chemo- and stereoselectively with a variety of aryl and alkenyl halides under palladium catalysis.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".