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Record W1918016093 · doi:10.1039/c4cc09107h

Facile synthesis of borofragments and their evaluation in activity-based protein profiling

2015· article· en· W1918016093 on OpenAlexafffund
Shinya Adachi, Armand B. Cognetta, Micah J. Niphakis, Zhi He, Adam Zajdlik, Jeffrey D. St. Denis, Conor C. G. Scully, Benjamin F. Cravatt, Andrei K. Yudin

Bibliographic record

VenueChemical Communications · 2015
Typearticle
Languageen
FieldMedicine
TopicPeptidase Inhibition and Analysis
Canadian institutionsUniversity of TorontoToronto Public Health
FundersNational Institute on Drug AbuseNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsProfiling (computer programming)Combinatorial chemistryChemistryComputer scienceComputational biologyBiologyProgramming language

Abstract

fetched live from OpenAlex

The discovery of enzyme inhibitors relies on synthetic methods that enable rapid and modular construction of small molecules. Heterocyclic fragments designed to maximize enthalpic interactions with their protein targets represent a particularly desirable class of molecules. Here we describe a reagent that enables straightforward construction of "borofragments", in which a heterocycle is separated from the boron center by two or three rotatable bonds. The stability of these molecules depends on the MIDA group which likely acts as a slow-release element under biological conditions. Borofragments can be used to discover inhibitors of enzymes that use catalytic oxygen nucleophiles. We have employed this method to identify inhibitors of ABHD10 and the predicted carboxypeptidase CPVL. This technique should be applicable to other classes of targets.

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 score0.030
Threshold uncertainty score0.212

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.107
GPT teacher head0.348
Teacher spread0.241 · 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

Citations37
Published2015
Admission routes2
Has abstractyes

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