Focused On-demand Library for Nuclear factor erythroid 2-related factor 2

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

The focused library is created on demand with the latest virtual screening and parameter assessment technology, supported by the Receptor.AI drug discovery platform. This method is more effective than traditional methods and results in higher-quality compounds with better activity, selectivity, and safety.

The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is facilitated by our partner Reaxense.

The library includes a list of the most promising modulators annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Also, each compound is presented with its optimal docking poses, affinity scores, and activity scores, providing a comprehensive overview.

We utilise our cutting-edge, exclusive workflow to develop focused libraries for enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

The method includes detailed molecular simulations of the catalytic and allosteric binding pockets, along with ensemble virtual screening that considers their conformational flexibility. In the design of modulators, structural changes induced by reaction intermediates are taken into account to enhance activity and selectivity.

Our library is unique due to several crucial aspects:

  • Receptor.AI compiles all relevant data on the target protein, such as past experimental results, literature findings, known ligands, and structural data, thereby enhancing the likelihood of focusing on the most significant compounds.
  • By utilizing advanced molecular simulations, the platform is adept at locating potential binding sites, rendering the compounds in the focused library well-suited for unearthing allosteric inhibitors and binders for hidden pockets.
  • The platform is supported by more than 50 highly specialized AI models, all of which have been rigorously tested and validated in diverse drug discovery and research programs. Its design emphasizes efficiency, reliability, and accuracy, crucial for producing focused libraries.
  • Receptor.AI extends beyond just creating focused libraries; it offers a complete spectrum of services and solutions during the preclinical drug discovery phase, with a success-dependent pricing strategy that reduces risk and fosters shared success in the project.







Alternative names:

HEBP1; Nuclear factor, erythroid derived 2, like 2

Alternative UPACC:

Q16236; B2RBU2; B4E338; E9PGJ7; Q53RW6; Q59HH2; Q96F71


Nuclear factor erythroid 2-related factor 2 (NFE2L2/NRF2) is a pivotal transcription factor in oxidative stress response. It regulates the expression of cytoprotective genes by binding to antioxidant response elements (ARE) in their promoters. Under normal conditions, NFE2L2/NRF2 is ubiquitinated and degraded, but oxidative stress leads to its accumulation and activation, promoting cell survival.

Therapeutic significance:

NFE2L2/NRF2 plays a crucial role in diseases characterized by oxidative stress and inflammation, including Immunodeficiency, developmental delay, and hypohomocysteinemia (IMDDHH). Understanding the role of NFE2L2/NRF2 could open doors to potential therapeutic strategies for these conditions.

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