Migrative Reductive Amination of Ketones Enabled by Multitasking Reagents
Bibliographic record
Abstract
Skeletal editing and “single-atom logic” are emerging strategies that accelerate compound synthesis and open new chemical space by modifying organic molecules using unconventional bond forming processes. These new strategies are particularly attractive to access important biologically active classes of compounds, such as secondary amines, which are key synthetic intermediates and components found in numerous pharmaceutical agents. Herein, a practical modification of the classical reductive amination of ketones and aldehydes, a staple reaction in drug discovery research, was developed to provide isomeric amines by way of a migratory reductive amination (MRA). This one-pot method combines three distinct chemical reactions in a single flask, without solvent changes, via the orchestrated addition of two inexpensive and non-toxic multitasking reagents: Zn(II) salts and a hydrosilane. Both reagents display exceptional orthogonality with a synergetic role in all three stages of the process, lending a procedure that embodies many of the ideals of green chemistry. This MRA method demonstrates a wide scope of acyclic and cyclic ketones and aldehydes with aliphatic or aromatic groups, including complex molecules such as drug intermediates and natural products with an exceptionally low E-factor compared to established methods. Remarkably, MRA enables the expeditious preparation of cyclic secondary amines of varying ring size, including a cyclopentanone-to-piperidine ring-edit that provides a direct access to the most common saturated heterocycle in drug discovery.
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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) | 0.001 | 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".