MétaCan
Menu
Back to cohort
Record W1981652396 · doi:10.1021/jm200532b

Inhibitors of Androgen Receptor Activation Function-2 (AF2) Site Identified through Virtual Screening

2011· article· en· W1981652396 on OpenAlexaff
Peter Axerio-Cilies, Nathan A. Lack, M. Ravi Shashi Nayana, Ka Hong Chan, Anthony T. Yeung, Eric Leblanc, Emma S. Tomlinson Guns, Paul S. Rennie, Artem Cherkasov

Bibliographic record

VenueJournal of Medicinal Chemistry · 2011
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsChemistryAndrogen receptorVirtual screeningAndrogenFunction (biology)ReceptorPharmacologyEndocrinologyInternal medicineBiochemistryCell biologyDrug discoveryHormoneProstate cancer

Abstract

fetched live from OpenAlex

The androgen receptor (AR) is one of the most studied drug targets for the treatment of prostate cancer. However, all current anti-androgens directly interact with the AR at the androgen binding site, which is prone to resistant mutations, calling for new strategies of the AR inhibition. The current study represents the first attempt to use virtual screening to identify inhibitors of activation function-2 (AF2) of the human AR. By combining large-scale docking with experimental approaches, we were able to identify several small molecules that interact with the AF2 and effectively prevent the transcriptional activation of the AR. The crystallographic structure of one of these inhibitors in complex with the AR provides critical insight into the corresponding protein-ligand interactions and suitable for future hit optimization. Taken together, our results provide a promising ground for development of novel anti-androgens that can help to address the problem of drug resistance in prostate cancer.

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: Simulation or modeling · Consensus signal: Simulation or modeling
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)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.048
GPT teacher head0.301
Teacher spread0.253 · 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 designSimulation or modeling
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

Citations96
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Medicinal ChemistrySame topicProstate Cancer Treatment and ResearchFrench-language works237,207