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Record W2345261027 · doi:10.1021/acs.jmedchem.6b00222

Passive Membrane Permeability of Macrocycles Can Be Controlled by Exocyclic Amide Bonds

2016· article· en· W2345261027 on OpenAlexafffund
Jennifer L. Hickey, Serge Zaretsky, Megan A. St. Denis, Sai Kumar Chakka, M. Monzur Morshed, Conor C. G. Scully, Andrew L. Roughton, Andrei K. Yudin

Bibliographic record

VenueJournal of Medicinal Chemistry · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChemical Synthesis and Analysis
Canadian institutionsAegera Therapeutics (Canada)University of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchOntario Genomics InstituteQuébec Consortium for Drug Discovery
KeywordsChemistryPeptidomimeticAmideIntramolecular forceCyclic peptideLinkerHydrogen bondStereochemistryPeptide bondAmino acidCombinatorial chemistryMembranePeptideOrganic chemistryMoleculeBiochemistry

Abstract

fetched live from OpenAlex

We have developed a strategy for synthesizing passively permeable peptidomimetic macrocycles. The cyclization chemistry centers on using aziridine aldehydes in a multicomponent reaction with peptides and isocyanides. The linker region in the resulting product contains an exocyclic amide positioned α to the peptide backbone, an arrangement that is not found among natural amino acids. This amide provides structural rigidity within the cyclic peptidomimetic and promotes the creation of a stabilizing intramolecular hydrogen bonding network. This exocyclic control element also contributes to the increased membrane permeability exhibited by multicomponent-derived macrocycles with respect to their homodetic counterparts. The exocyclic control element is employed along with a strategic placement of N-methyl and d-amino acids to produce passively permeable peptides, which contain multiple polar residues. This strategy should be applicable in the pursuit of synthesizing therapeutically relevant macrocycles.

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)0.001
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 score0.004
Threshold uncertainty score0.434

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.005
GPT teacher head0.231
Teacher spread0.226 · 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

Citations56
Published2016
Admission routes2
Has abstractyes

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