<i>Lewis</i> Acid‐Mediated Addition of Amino Acid‐Substituted <i>α</i>‐Allylsilanes to Aromatic Acetals
Bibliographic record
Abstract
Abstract Unnatural amino acids extend the pharmacological formulator's toolkit. Strategies to prepare unnatural amino acid derivatives using Lewis acid‐activated allylsilane reactions are few. In this regard, we examined the utility of allylsilanes bearing an amino acid substituent in the reaction. Diastereoselective addition of methyl 2‐(N‐PG‐amino)‐3‐(trimethylsilyl)pent‐4‐enoate and methyl (E)‐2‐(N‐PG‐amino)‐3‐(trimethylsilyl)hex‐4‐enoate (PG=protecting group), 2 and 13, respectively, to aromatic acetals in the presence of Lewis acids is described. Of those examined, TiCl4 was found to be the most effective Lewis acid for promoting the addition. At least 1 equiv. of TiCl4 was required to achieve high yields, whereas 2 equiv. of BF3⋅OEt2 were required for comparable outcomes. Excellent selectivity (>99% syn/anti) and high yield (up to 89%) were obtained with halo‐substituted aromatic acetals, while more electron‐rich electrophiles led to both lower yields and diastereoselectivities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".