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Record W3090375091 · doi:10.1126/scitranslmed.aba8412

Bicyclic azetidines kill the diarrheal pathogen <i>Cryptosporidium</i> in mice by inhibiting parasite phenylalanyl-tRNA synthetase

2020· article· en· W3090375091 on OpenAlexaff
Sumiti Vinayak, Rajiv S. Jumani, Peter G. Miller, Muhammad M. Hasan, Briana McLeod, Jayesh Tandel, Erin E. Stebbins, José E. Teixeira, Julien Borrel, Arthur Gonse, Mingliang Zhang, Xianshui Yu, Amy K. Wernimont, Chris Walpole, Sean Eckley, Melissa S. Love, Case W. McNamara, Manmohan Sharma, Amit Sharma, Christina Scherer, Nobutaka Kato, Stuart L. Schreiber, Bruno Melillo, Boris Striepen, Christopher D. Huston, Eamon Comer

Bibliographic record

VenueScience Translational Medicine · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicParasitic Infections and Diagnostics
Canadian institutionsStructural Genomics Consortium
FundersNational Institute of Allergy and Infectious DiseasesBill and Melinda Gates Foundation
KeywordsCryptosporidiumParasite hostingPathogenMicrobiologyVirologyBiologyChemistryFeces

Abstract

fetched live from OpenAlex

PheRS. Medicinal chemistry optimization led to the identification of an optimal pharmacokinetic/pharmacodynamic profile for this series. Collectively, these data demonstrate that bicyclic azetidines are a promising series for anticryptosporidial drug development and establish a broad framework to enable target-based drug discovery for this infectious disease.

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)0.001
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 score0.357
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.000
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 head0.015
GPT teacher head0.265
Teacher spread0.250 · 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

Citations82
Published2020
Admission routes1
Has abstractyes

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