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Record W4409896614 · doi:10.1039/d5sc01296a

Sequence-defined peptoids <i>via</i> iterative exponential growth

2025· article· en· W4409896614 on OpenAlexafffund
Abigail Mae Clapperton, Christine Hood, Helen Tran

Bibliographic record

VenueChemical Science · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChemical Synthesis and Analysis
Canadian institutionsUniversity of Toronto
FundersDivision of Electrical, Communications and Cyber SystemsNatural Sciences and Engineering Research Council of CanadaUniversity of TorontoCanada First Research Excellence FundSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsSequence (biology)Exponential growthExponential functionScalabilityAlgorithmComputer scienceMathematicsChemistryBiochemistry

Abstract

fetched live from OpenAlex

Synthetic control over polymer sequence, composition, and stereochemistry is critical to understanding their influence on the intramolecular and intermolecular interactions of polymers. We report an iterative exponential growth (IEG) strategy for peptoids, a class of sequence-defined peptidomimetics, relying on orthogonally protected monomers. The IEG technique enables the synthesis of monodisperse peptoids with varied sequences, side chains, and stereoconfigurations on a scale that is useful for material science applications. The method allows for direct monitoring of the reaction progress without the need for cleavage from a solid-support. This IEG strategy offers higher molecular weights than other solution-phase sequence-defined synthetic strategies for peptoids and seeks to mimic the precise structural organization of sequence-defined biopolymers for a synthetic polymer system, which we anticipate will enable the rational design of functional polymer materials.

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 score0.014
Threshold uncertainty score0.457

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.255
Teacher spread0.247 · 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

Citations5
Published2025
Admission routes2
Has abstractyes

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