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Related Experiment Video

Updated: Sep 15, 2025

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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Identification of a Novel Indolizine RORγT Inverse Agonist Using the AI-Driven Drug Design Platform.

Rafał A Bachorz1, Joanna Pastwińska2, Michael S Lawless1

  • 1Simulations Plus, Inc., P.O. Box 12317, Research Triangle Park, North Carolina 27709, United States.

ACS Medicinal Chemistry Letters
|July 16, 2025
PubMed
Summary

Automated multiparameter optimization (MPO) using AI-driven drug design (AIDD) created novel RORγT ligands. Many compounds showed RORγT inhibition and favorable ADMET properties, with one potent compound demonstrating therapeutic potential.

Keywords:
RORγTartificial intelligencedrug designinverse agonist

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Area of Science:

  • Medicinal Chemistry
  • Computational Drug Design
  • Pharmacology

Background:

  • Drug development requires efficient optimization of multiple molecular properties.
  • Nuclear receptor modulators are critical therapeutic targets.
  • AI-driven drug design (AIDD) offers a novel approach to accelerate discovery.

Purpose of the Study:

  • To design novel Retinoid-related Orphan Receptor gamma (RORγT) ligands using an AIDD platform.
  • To optimize ligands for activity, ADMET properties, novelty, and synthetic feasibility.
  • To validate the computational predictions through experimental assays.

Main Methods:

  • Employed an AI-driven drug design (AIDD) platform for ligand generation.
  • Utilized Quantitative Structure-Activity Relationship (QSAR) models and machine learning for property prediction.
  • Applied multicriteria decision analysis (MCDA) for compound selection.
  • Conducted cell-based assays to measure RORγT inhibition and in vitro ADMET profiling.

Main Results:

  • 70% of selected compounds inhibited RORγT activity (>25% at 20 μM).
  • The most potent compound exhibited an IC50 of 1.51 μM and demonstrated activity in human T cells.
  • In vitro ADMET properties (solubility, clearance, permeability) were favorable and aligned with predictions.

Conclusions:

  • AIDD combined with MPO effectively generates novel, active RORγT ligands with desirable drug-like properties.
  • The identified indolizine scaffold represents a novel chemical series for RORγT modulation.
  • This approach accelerates early-stage drug design, yielding promising candidates for further development.