Focused On-demand Library for Egl nine homolog 1

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

This comprehensive focused library is produced on demand with state-of-the-art virtual screening and parameter assessment technology driven by Receptor.AI drug discovery platform. This approach outperforms traditional methods and provides higher-quality compounds with superior activity, selectivity and safety.

We carefully select specific compounds from a vast collection of over 60 billion molecules in virtual chemical space. Our partner Reaxense helps in synthesizing and delivering these compounds.

The library features a range of promising modulators, each detailed with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Plus, each compound is presented with its ideal docking poses, affinity scores, and activity scores, ensuring a thorough insight.

We employ our advanced, specialised process to create targeted libraries for enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

The procedure entails thorough molecular simulations of the catalytic and allosteric binding pockets, accompanied by ensemble virtual screening that factors in their conformational flexibility. When developing modulators, the structural modifications brought about by reaction intermediates are factored in to optimize 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:

Hypoxia-inducible factor prolyl hydroxylase 2; Prolyl hydroxylase domain-containing protein 2; SM-20

Alternative UPACC:

Q9GZT9; Q8N3M8; Q9BZS8; Q9BZT0


Egl nine homolog 1 (EGLN1), also known as Hypoxia-inducible factor prolyl hydroxylase 2, plays a pivotal role as a cellular oxygen sensor. It catalyzes the formation of 4-hydroxyproline in HIF alpha proteins under normoxic conditions, leading to their degradation. This process is crucial for the regulation of hypoxia-inducible genes, influencing angiogenesis and cardiac functionality.

Therapeutic significance:

EGLN1's involvement in familial erythrocytosis, a condition characterized by elevated hemoglobin and hematocrit levels, underscores its potential as a therapeutic target. Understanding EGLN1's role could pave the way for innovative treatments for hypoxia-related diseases.

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