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Record W2401913982 · doi:10.1021/acs.jmedchem.5b00432

Tankyrase 1 Inhibitors with Drug-like Properties Identified by Screening a DNA-Encoded Chemical Library

2015· article· en· W2401913982 on OpenAlexaff
Florent Samain, T. Ekblad, Gediminas Mikutis, Nan Zhong, Mauro Zimmermann, Angela Nauer, Davor Bajic, Willy Decurtins, Jörg Scheuermann, Peter J. Brown, Jonathan Hall, S. Gräslund, H. Schüler, Dario Neri, Raphael M. Franzini

Bibliographic record

VenueJournal of Medicinal Chemistry · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiochemical and Molecular Research
Canadian institutionsStructural Genomics ConsortiumUniversity of Toronto
FundersDirectorate-General for Research and InnovationEidgenössische Technische Hochschule ZürichIngaBritt och Arne Lundbergs ForskningsstiftelseStiftelsen för Strategisk ForskningMinistero dello Sviluppo EconomicoSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNovartis Foundation
KeywordsChemistryChemical libraryDrugPolymeraseDNACombinatorial chemistryDrug discoveryPoly ADP ribose polymeraseIC50Chemical synthesisStereochemistryBiochemistryComputational biologyIn vitroSmall moleculePharmacologyBiology

Abstract

fetched live from OpenAlex

We describe the synthesis and screening of a DNA-encoded chemical library containing 76230 compounds. In this library, sets of amines and carboxylic acids are directly linked producing encoded compounds with compact structures and drug-like properties. Affinity screening of this library yielded inhibitors of the potential pharmaceutical target tankyrase 1, a poly(ADP-ribose) polymerase. These compounds have drug-like characteristics, and the most potent hit compound (X066/Y469) inhibited tankyrase 1 with an IC50 value of 250 nM.

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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.230
Teacher spread0.215 · 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

Citations64
Published2015
Admission routes1
Has abstractyes

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