The Targeted AHR Modulator Library is a curated chemical screening set designed to accelerate the discovery of novel small-molecule modulators of the aryl hydrocarbon receptor (AHR) — a ligand-dependent transcription factor with key roles in immunity, cancer, inflammation, and xenobiotic metabolism.

This unique compound library supports early-stage discovery, target validation, and therapeutic innovation by providing access to structurally diverse and computationally prioritized molecules tailored for AHR-centric research and drug development programs.

 Scientific Rationale: Why Target AHR?

The AHR plays a central role in:

• Regulating immune tolerance and inflammation
• Modulating the tumor microenvironment and acting as an immune checkpoint-like regulator
• Maintaining epithelial barrier integrity (skin, gut, lungs)
• Mediating responses to environmental and dietary compounds
• Interfacing with the gut microbiome and controlling local immune responses

Therapeutic potential includes:

• Oncology (e.g., AHR antagonists like BAY 2416964, IK-175)
• Inflammatory skin diseases (e.g., Tapinarof, an AHR agonist for psoriasis)
• Autoimmune and metabolic diseases (e.g., type 1 diabetes, IBD, AIH)
• Neuroinflammation and neurodegenerative disorders (e.g., Alzheimer’s)

 Library Overview

Number of Compounds: 1,698 unique, purchasable small molecules
Design Focus: Ligand binding to the AHR PAS-B domain (the primary binding pocket)
Applications: High-throughput screening, phenotypic assays, structure-activity relationship studies, and lead optimization

 How the Library Was Built: Multi-Layered Design Strategy

1. Physicochemical Filtering

To ensure drug-likeness and bioavailability, compounds were selected based on:

Property	Range
Hydrogen Bond Donors (HBD)	0–4
Hydrogen Bond Acceptors (HBA)	0–8
Molecular Weight	100–420
LogP (Lipophilicity)	0–6
Rotatable Bonds	0–8
TPSA (Topological Polar Surface Area)	0–120

2. Molecular Similarity Clustering

To reduce redundancy and maximize chemical diversity:

• Tanimoto coefficient ≥ 0.5 using 256-bit ECFP6 fingerprints
• Retained chemotypes associated with AHR activity (e.g., indoles, halogenated aromatics)

3. Machine Learning Prediction

We integrated XGBoost classifiers trained on the Tox21 AHR assay dataset:

• Descriptors: ECFP4, 1024-bit
• Average AUC ROC: 0.90
• Prioritized compounds with high predicted AHR binding likelihood

4. Structure-Based Docking

Focused virtual screening against the AHR PAS-B domain (PDB ID: 7ZUB, 8QMO):

• Docking scores computed via HTVS pipeline
• Cutoff threshold: < –10.0 kcal/mol
• Reference compounds:
  • Indirubin: –11.7 kcal/mol
  • Benzo[a]pyrene: –10.7 kcal/mol
  • Top library hit: –13.0 kcal/mol

 Key Advantages

• Mechanistically Relevant: Ligand design centered on the AHR PAS-B domain, ensuring target-specific interactions
• Disease-Oriented Applications: Designed for therapeutic discovery across oncology, dermatology, immunology, and neuroinflammation
• Multi-disciplinary Compatibility: Ideal for in vitro, in silico, and phenotypic screening workflows
• Integrates Best Practices: Combines state-of-the-art machine learning, cheminformatics, and structure-based design

 Use Cases in Discovery Programs

 Drug Discovery & Optimization

• Rapid hit identification via reporter assays (e.g., AHR-XRE luciferase)
• Follow-up in SAR studies and analog expansion
• Identify agonists, antagonists, or SAhRMs (selective AHR modulators)

 High-Content Phenotypic Screening

• Immune modulation in T-cell co-culture models
• Anti-inflammatory screening in epithelial cell systems (skin, gut, lung)

 Preclinical Mechanistic Studies

• Study xenobiotic metabolism (CYP1A1/1B1)
• Evaluate ligand-specific AHR conformational switching
• De-risking toxicological profiles of AHR-active scaffolds

 Insights from Literature & Benchmarking

This collection builds on validated workflows used to identify compounds like:

• BAY 2416964: AHR antagonist in cancer immunotherapy
• Tapinarof: First FDA-approved topical AHR modulator
• GNF-351, CH-223191: Benchmark synthetic antagonists
• Punicalagin, FICZ: Natural ligands with immunomodulatory profiles

Combined with conformational switch analyses, species-specific pharmacology, and ultrasensitivity testing, this collection is ready for translational deployment.

 Ready to Accelerate Your AHR Program?

We offer:

• Custom reformatting and plating options
• In silico support for docking and virtual screening
• Optional follow-up analog library design
• Integration with bioactivity data and cell-based screening platforms

 

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Key References:

 AHR Biology & Function

1. Stockinger, B., Di Meglio, P., Gialitakis, M., & Duarte, J. H. (2014). The aryl hydrocarbon receptor: Multitasking in the immune system. Annual Review of Immunology, 32, 403–432. https://doi.org/10.1146/annurev-immunol-032713-120121

 A landmark review outlining AHR’s central role in immune regulation, including its impact on T cell differentiation and inflammatory signaling.
→ Essential for understanding why AHR is a high-value immunological target.

2. Rothhammer, V., & Quintana, F. J. (2019). The aryl hydrocarbon receptor: An environmental sensor integrating immune responses in health and disease. Nature Reviews Immunology, 19(3), 184–197. https://doi.org/10.1038/s41577-019-0125-8

 Connects AHR to environmental sensing, host-microbiome interaction, and systemic immunity.
→ Offers a modern context for AHR’s relevance in chronic diseases.

3. Murray, I. A., Patterson, A. D., & Perdew, G. H. (2014). AHR function in cancer: A paradigm for the diversity of ligand-dependent AHR signaling. Nature Reviews Cancer, 14(12), 801–814. https://doi.org/10.1038/nrc3846

 Explores AHR’s dual roles in tumor promotion and suppression, supporting its emerging role as a cancer drug target.

 Library Development & Computational Strategies

4. Mosa, F. E. S., El-Kadi, A. O. S., & Barakat, K. (2021). Targeting the aryl hydrocarbon receptor (AhR): A review of the in-silico screening approaches to identify AhR modulators. In InTechOpen. https://doi.org/10.5772/intechopen.99228

 Covers ligand docking, homology modeling, and virtual screening strategies—directly relevant to the library’s structure-based design.

5. Mosa, F. E. S., AlRawashdeh, S., El-Kadi, A. O. S., & Barakat, K. (2024). Investigating the aryl hydrocarbon receptor agonist/antagonist conformational switch using well-tempered metadynamics simulations. Journal of Chemical Information and Modeling. https://doi.org/10.1021/acs.jcim.4c00169

 Provides deep insight into ligand-induced conformational dynamics at the AHR PAS-B domain, supporting rational modulator design.

6. Hesse, J., Boldini, D., & Sieber, S. A. (2024). Machine learning-driven data valuation for optimizing high-throughput screening pipelines. Journal of Chemical Information and Modeling. https://doi.org/10.1021/acs.jcim.4c01547

 Demonstrates the integration of ML (e.g., XGBoost) for compound prioritization—a core component of the library strategy.

 Therapeutic Relevance and Benchmarking

7. Kober, C., Roewe, J., Schmees, N., Roese, L., Roehn, U., Bader, B., ... & Gutcher, I. (2023). Targeting the aryl hydrocarbon receptor (AhR) with BAY 2416964: A selective small molecule inhibitor for cancer immunotherapy. Journal for ImmunoTherapy of Cancer, 11, e007495. https://doi.org/10.1136/jitc-2023-007495

 Shows preclinical validation of AHR antagonists as immunotherapeutics, including use of HTS and tumor models—benchmark for library applications.

8. Chen, J., Haller, C. A., Jernigan, F. E., Koerner, S. K., Wong, D. J., Wang, Y., ... & Chaikof, E. L. (2020). Modulation of lymphocyte-mediated tissue repair by rational design of heterocyclic aryl hydrocarbon receptor agonists. Science Advances, 6(3), eaay8230. https://doi.org/10.1126/sciadv.aay8230

 Highlights the power of rational AHR ligand design to modulate immune repair—illustrating real-world application of library hits.

 Natural Ligand Screening & Optimization

9. Dai, W., Yin, S., Wang, F., Kuang, T., Xiao, H., Kang, W., ... & Zhu, J. (2024). Punicalagin as a novel selective aryl hydrocarbon receptor (AhR) modulator upregulates AhR expression through the PDK1/p90RSK/AP-1 pathway to promote the anti-inflammatory response and bactericidal activity of macrophages. Cell Communication and Signaling, 22, 33. https://doi.org/10.1186/s12964-024-01847-9

 Exemplifies natural product-based discovery of novel SAhRMs, a critical use case of the AHR modulator library.

10. Bobrovs, R., Terentjeva, S., Olafsen, N. E., Dambrauskas, Ž., Gulbinas, A., Maimets, T., ... & Jaudzems, K. (2024). Discovery and optimisation of pyrazolo[1,5-a]pyrimidines as aryl hydrocarbon receptor antagonists. RSC Medicinal Chemistry, 15(3), 436–448. https://doi.org/10.1039/d4md00266k

 Provides a SAR-guided case study of AHR-targeted compound optimization—highlighting paths for further development from library hits.