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Record W2765193965 · doi:10.1111/cbdd.13132

Perspectives on the discovery of NOTCH2‐specific inhibitors

2017· review· en· W2765193965 on OpenAlexafffund
Peter Dobranowski, Fuqiang Ban, Alberto Contreras‐Sanz, Artem Cherkasov, Peter C. Black

Bibliographic record

VenueChemical Biology & Drug Design · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDevelopmental Biology and Gene Regulation
Canadian institutionsBC Children's HospitalUniversity of British Columbia
FundersNational Cancer InstitutePfizerCanadian Cancer Society Research InstituteBristol-Myers Squibb
KeywordsNotch signaling pathwayCarcinogenesisBiologyNotch proteinsComputational biologySignal transductionDrug discoveryCell biologyEpitopeIntracellularSmall moleculeStructural similarityExtracellularCancer researchGeneBioinformaticsBiochemistryGeneticsAntibody

Abstract

fetched live from OpenAlex

The Notch pathway is a cell-cell communication system where membrane-bound ligands interact with the extracellular region of Notch receptors to induce intracellular, downstream effects on gene expression. Aberrant Notch signaling promotes tumorigenesis, and the Notch pathway has tremendous potential for novel targeting strategies in cancer treatment. While γ-secretase inhibitors as Notch-inhibiting agents are already promising in clinical trials, they are highly non-specific with adverse side-effects. One of the underlying challenges is that two of the four known human Notch paralogs, NOTCH1 and 2, share very high structural similarity but play opposing roles in some tumorigenesis pathways. This perspective explores the feasibility of developing Notch-specific small molecule inhibitors targeting the anti-NOTCH2 antibody-binding epitopes or the "S2-Leu-plug-binding site" using a computer-aided drug discovery approach.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.005

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 head0.058
GPT teacher head0.319
Teacher spread0.261 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations9
Published2017
Admission routes2
Has abstractyes

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