Bibliographic record
Abstract
Molecular scaffolds have been shown to facilitate and stabilise secondary structural turn elements, with a central core-arranging functionality in a defined three-dimensional orientation. In a peptide-based molecular imaging probe, this approach is of particular value as it would essentially "hide" a metal radioisotope within the ligand framework, making the labelling element a critical component of the receptor-bound structure. Starting from a 1,2-diaminoethane loaded 2-chlorotrityl resin, a versatile set of triamine ligand systems were synthesised by using solid-phase Fmoc-based peptide chemistry. The resultant resin-bound peptides then underwent amide reduction by treatment with borane-THF at 65 °C. This provided complete conversion to the corresponding polyamine entities in high purity for the majority of the amino acids utilised. The triamines were then coordinated on solid support by using [NEt(4)](2)[Re(CO)(3)(Br)(3)] followed by resin cleavage and HPLC purification, to give the desired rhenium coordinated species. We have shown that amino acid sequences can be assembled, reduced and coordinated on-resin, resulting in a versatile set of metal-ligand constructs. These studies could be expanded to generate libraries of turn-based peptidomimetics containing Re/Tc(I) organometallic scaffolds, with the intention of developing an improved approach for finding new diagnostic and therapeutic radiopharmaceutical entities.
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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) | 0.000 | 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 | 0.001 |
| 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".