Synthesis of Azido Acids and Their Application in the Preparation of Complex Peptides
Bibliographic record
Abstract
Abstract Azido acids are important synthons for the synthesis of complex peptides. As a protecting group, the azide moiety is atom-efficient, easy to install and can be reduced in the presence of many other protecting groups, making it ideal for the synthesis of branched and/or cyclic peptides. α-Azido acids are less bulky than urethane-protected counterparts and react more effectively in coupling reactions of difficult-to-form peptide and ester bonds. Azido acids can also be used to form azoles on complex intermediates. This review covers the synthesis of azido acids and their application to the total synthesis of complex peptide natural products. 1 Introduction 2 Synthesis of α-Azido Acids 2.1 From α-Amino Acids or Esters 2.2 Via α-Substitution 2.3 Via Electrophilic Azidation 2.4 Via Condensation of N-2-Azidoacetyl-4-Phenylthiazolidin- 2-Thi one Enolates with Aldehydes and Acetals 2.5 Synthesis of α,β-Unsaturated α-Azido Acids and Esters 3 Synthesis of β-Azido Acids 3.1 Preparation of Azidoalanine and 3-Azido-2-aminobutanoic Acids 3.2 General Approaches to Preparing β-Azido Acids Other Than Azi doalanine and AABA 4 Azido Acids in Total Synthesis 4.1 α-Azido Acids 4.2 β-Azido Acids and Azido Acids Containing an Azide on the Side Chain 5 Conclusions
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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".