Focused On-demand Library for Prolyl hydroxylase EGLN3

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

The specialised, focused library is developed on demand with the most recent virtual screening and parameter assessment technology, guided by the Receptor.AI drug discovery platform. This approach exceeds the capabilities of traditional methods and offers compounds with higher activity, selectivity, and safety.

From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Our collaborator, Reaxense, aids in their synthesis and provision.

In the library, a selection of top modulators is provided, each marked with 38 ADME-Tox and 32 parameters related to physicochemical properties and drug-likeness. Also, every compound comes with its best 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

This approach involves comprehensive molecular simulations of the catalytic and allosteric binding pockets and ensemble virtual screening that accounts for their conformational flexibility. In the case of designing modulators, the structural adjustments caused by reaction intermediates are considered to improve activity and selectivity.

Several key aspects differentiate our library:

  • Receptor.AI compiles an all-encompassing dataset on the target protein, including historical experiments, literature data, known ligands, and structural insights, maximising the chances of prioritising the most pertinent compounds.
  • The platform employs state-of-the-art molecular simulations to identify potential binding sites, ensuring the focused library is primed for discovering allosteric inhibitors and binders of concealed pockets.
  • Over 50 customisable AI models, thoroughly evaluated in various drug discovery endeavours and research projects, make Receptor.AI both efficient and accurate. This technology is integral to the development of our focused libraries.
  • In addition to generating focused libraries, Receptor.AI offers a full range of services and solutions for every step of preclinical drug discovery, with a pricing model based on success, thereby reducing risk and promoting joint project success.







Alternative names:

Egl nine homolog 3; HPH-1; Hypoxia-inducible factor prolyl hydroxylase 3; Prolyl hydroxylase domain-containing protein 3

Alternative UPACC:

Q9H6Z9; Q2TA79; Q3B8N4; Q6P1R2


Prolyl hydroxylase EGLN3, also known as Hypoxia-inducible factor prolyl hydroxylase 3, plays a pivotal role in oxygen sensing and cellular response to hypoxia. It hydroxylates proline residues in various proteins, including HIF1A and HIF2A, under normoxic conditions, leading to their degradation. This process is attenuated under hypoxic conditions, allowing HIFs to activate the transcription of hypoxia-inducible genes. EGLN3 also influences glycolysis, apoptosis in cardiomyocytes and neurons, neutrophilic inflammation, and the DNA damage response.

Therapeutic significance:

Understanding the role of Prolyl hydroxylase EGLN3 could open doors to potential therapeutic strategies.

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