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<b>Identification of Novel Modulators of RORγt by Rational Drug Design and Molecular Docking</b>

2025· other· W7094959907 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2025
Typeother
Language
FieldSocial Sciences
TopicIrish and British Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDrug designRational designDrug discoveryDrugRetinoic acidVirtual screening

Abstract

fetched live from OpenAlex

This abstract was presented at the IVth International Drug Discovery and Development Forum 2018, held in Montreal, QC, Canada, on 22–24 October 2018. The work describes the discovery of novel small-molecule hits targeting retinoic acid receptor-related orphan receptor gamma t (RORγt), a key transcription factor regulating Th17 and Tc17 differentiation in highly sensitized renal-transplant candidates. The study focused on the pharmacological and immunological evaluation of these hit compounds, particularly TF-S1 and TF-S2, which were identified as potent RORγt inverse-agonist hits.Using a combination of rational drug design and time-resolved fluorescence resonance energy transfer (TR-FRET) assays, a new set of RORγt inverse-agonist hits was identified. TF-S1 and TF-S2 demonstrated strong binding affinity and potent inhibition of Th17-associated cytokines in vitro and showed promise as immunomodulatory agents for preventing antibody-mediated rejection in solid-organ transplantation.Correction notice: The original proceedings version listed compound TF-S1 as a partial agonist. Subsequent confirmatory experiments demonstrated that TF-S1 also behaves as an inverse agonist. This reprint version reflects the corrected interpretation in the Results, Conclusion, and Significance sections.Presented at: IVth International Drug Discovery and Development Forum Date: October 24, 2018 Location: Montreal, QC, Canada Category: Drug Design and Synthesis Presentation Type: Oral and Poster PresentationAuthors: Ahmed Fouda, Sarita Negi, Steven Paraskevas, Jean Tchervenkov Affiliation: Division of Surgical and Interventional Sciences (formerly Department of Experimental Surgery), Department of Surgery, McGill University, Montreal, QC, Canada📚 Full CitationFouda, A.; Negi, S.; Paraskevas, S.; Tchervenkov, J. Identification of Novel Modulators of RORγt by Rational Drug Design and Molecular Docking. In Proc. Int. Drug Discov. Dev. Forum 2018; 4, 12 (Abstr. 13). IVth International Drug Discovery and Development Forum, Montreal, QC, Canada, October 22–24, 2018. McGill University. DOI: https://doi.org/10.6084/m9.figshare.30418093. © 2018, Ahmed Fouda.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.006

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 head0.021
GPT teacher head0.276
Teacher spread0.254 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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