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Record W2034396242 · doi:10.1021/jo034167x

Syntheses and Activities of New C<sup>10</sup> β-Turn Peptidomimetics

2004· article· en· W2034396242 on OpenAlexafffund
Hong Boon Lee, Maria Clara Zaccaro, Mookda Pattarawarapan, Sudipta Raha Roy, H. Uri Saragovi, Kevin Burgess

Bibliographic record

VenueThe Journal of Organic Chemistry · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChemical Synthesis and Analysis
Canadian institutionsMcGill University
FundersNational Cancer InstituteMcGill University
KeywordsPeptidomimeticChemistryDipeptidePharmacophoreCombinatorial chemistryAmine gas treatingSulfonamideNitroStereochemistryGuanidineMoleculeTrk receptorAmino acidNeurotrophinReceptorPeptideOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

A program to identify small molecules that mimic or disrupt protein-protein interactions led us to design the peptidomimetics 1-3. Solid-phase syntheses of 1-3 were developed. The purities of the crude materials isolated from the resin tend to be highest for the S- and N-compounds 2 and 3 and better than in the corresponding syntheses of peptidomimetics A. The particular dipeptide units incorporated were chosen to correspond with the turn regions of the neurotrophins (e.g., nerve growth factor [NGF] and the neurotrophin factor-3 [NT-3]). Preliminary studies were performed to access the binding of these analogues to Trk receptors and their ability to induce cell survival (just as NGF and NT-3 do). Several active compounds were identified. However, poor water solubilities of some of the other compounds preclude reliable testing. Consequently, solid-phase modifications to the synthetic procedures were investigated to provide access to the derivatives 12-14 in which the aromatic nitro group is replaced by amine, guanidine, or sulfonamide functionalities. The latter are more acceptable pharmacophores than nitro groups and also tend to increase the water solubilities of the peptidomimetics.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.443

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.213
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations28
Published2004
Admission routes2
Has abstractyes

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