Synthesis and Applications of β‐Aminoalkylboronic Acid Derivatives
Bibliographic record
Abstract
Abstract α‐Aminoalkylboronic acids display a distinct role in medicinal chemistry, and their utility has been demonstrated by the successful commercialization of three drugs: bortezomib, ixazomib, and vaborbactam. Just as α‐aminoalkylboronic acids are a bioisostere of α‐amino acids, β‐aminoalkylboronates are a bona fide bioisostere of β‐amino acids, thus they also hold promising potential in drug discovery. Moreover, β‐aminoalkylboronates are versatile synthetic intermediates that are amenable to many of the established C−B bond derivatization reactions of chiral optically enriched alkylboronates, leading to the stereocontrolled preparation of valued classes of products such as β‐amino alcohols, 1,2‐diamines, and hemiboronic acid heterocycles. In addition, β‐aminoalkylboronates were shown to act as catalysts in certain organic reactions. This review presents an overview of the strengths and limitations of current preparative methods to access β‐aminoalkylboronic acid derivatives stereoselectively with various substitution patterns. Strategically, several disconnections can be exploited to establish both functional groups. Some of the key methods include the classical Matteson asymmetric homologation chemistry, transition metal‐catalyzed aminoboration of alkenes and formal hydroboration of enamine derivatives, nucleophilic additions of boryl‐substituted carbanions onto N ‐functionalized imines, borylative ring openings of aziridines, and functionalization of alpha‐boryl aldehydes. magnified image
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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) | 0.000 | 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) | 0.002 | 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".