Synthetic Strategies for Macrocyclic Peptides
Bibliographic record
Abstract
Peptide macrocycles form an outstanding class of natural and synthetic bioactive compounds. This chapter discusses synthetic strategies for the final ring-closing reaction by the widely employed and versatile processes of lactamization, lactonization, and disulfide bridge formation. According to the nature of the chemical bond found in the backbone, cyclic peptides can be classified in two major categories: homodetic peptides and heterodetic peptides. In principle, all methods suitable for peptide bond formation can be applied for head-to-tail macrocyclization of linear peptides; however the reaction usually proceeds more slowly than the corresponding bimolecular version. During synthesis design, the C-terminal amino acid of the linear precursor and the coupling reagent should be carefully chosen to minimize epimerization at the C-terminal residue during cyclization. In many cases, the solution-phase strategy is the best choice for performing the macrocyclization step, especially when larger quantities of cyclic peptide are required.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".