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Record W4409889295 · doi:10.1021/acs.jmedchem.5c00220

Broad Target Screening Reveals Abundance of FKBP12-Based Molecular Glues in Focused Libraries

2025· article· en· W4409889295 on OpenAlexafffund
Johannes K. Dreizler, Christian Meyners, Wisely Oki Sugiarto, Maximilian Repity, Edvaldo Vasconcelos Soares Maciel, Patrick L. Purder, Frederik Lermyte, Stefan Knapp, Felix Hausch

Bibliographic record

VenueJournal of Medicinal Chemistry · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSignaling Pathways in Disease
Canadian institutionsStructural Genomics Consortium
FundersStructural Genomics ConsortiumBundesministerium für Bildung und ForschungDeutsche Forschungsgemeinschaft
KeywordsFKBPDrug discoveryChemistryComputational biologyDrug repositioningDrugBiochemistryPharmacologyBiology

Abstract

fetched live from OpenAlex

Competitive (nondegradative) molecular glues represent a promising drug modality that remains underexplored primarily due to the lack of adequate hit identification approaches. In this study, we screened our historically grown FKBP-focused library containing >1000 drug-like molecules to identify FKBP-assisted molecular glues targeting a diverse panel of 57 proteins. In addition to establishing a robust and generalizable screening approach, we discovered three novel FKBP-dependent molecular glues targeting PTPRN, BRD4 BD2, and STAT4. Our results demonstrate that molecular glues are more common than previously thought and that they can be identified by repurposing existing focused libraries. An optimized, highly cooperative FKBP12-BRD4 BD2 glue demonstrated the involvement of the BD2 pocket and exhibited selectivity over the closely related BD1 domain. Our results underscore the value of FKBP12-assisted molecular glues to target challenging proteins with the potential for high selectivity.

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 score0.019
Threshold uncertainty score0.668

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.260
Teacher spread0.252 · 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

Citations7
Published2025
Admission routes2
Has abstractyes

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