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Record W2790351363 · doi:10.4052/tigg.1729.1se

Synthetic Inhibitors of Galectins: Structures and Syntheses

2018· article· en· W2790351363 on OpenAlexafffund
Vincent Denavit, Danny Lainé, Thomas Tremblay, Jacob St‐Gelais, Denis Giguère

Bibliographic record

VenueTrends in Glycoscience and Glycotechnology · 2018
Typearticle
Languageen
FieldImmunology and Microbiology
TopicGalectins and Cancer Biology
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaUniversité Laval
KeywordsGalectinGlycoconjugateDrug discoveryComputational biologyChemistryBiologyBiochemistry

Abstract

fetched live from OpenAlex

The recent discovery of the critical involvement of galectins in cancer progression, and in inflammatory and immune responses, has raised this family of β-D-galactoside-binding proteins to the rank of high-priority drug targets by the scientific community. This report will highlight the relevance of glycochemistry toward the efficient development of synthetic galectins inhibitors with high affinity and selectivity, as small molecules or multivalent glycoconjugates.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.013
GPT teacher head0.261
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), 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

Citations23
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueTrends in Glycoscience and GlycotechnologySame topicGalectins and Cancer BiologyFrench-language works237,207