Focused On-demand Library for Ubiquitin-conjugating enzyme E2 E3

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 use our state-of-the-art dedicated workflow for designing 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.

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:

E2 ubiquitin-conjugating enzyme E3; UbcH9; Ubiquitin carrier protein E3; Ubiquitin-conjugating enzyme E2-23 kDa; Ubiquitin-protein ligase E3

Alternative UPACC:

Q969T4; B2RAD6; D3DPG3; Q5U0R7; Q7Z4W4


The Ubiquitin-conjugating enzyme E2 E3, also known as UbcH9, plays a pivotal role in protein ubiquitination, a critical process for protein degradation and signaling. This enzyme is capable of catalyzing 'Lys-11', 'Lys-48', and 'Lys-63' linked polyubiquitination, highlighting its versatility in post-translational modification. Its involvement in the regulation of transepithelial sodium transport in renal cells underscores its importance in cellular homeostasis.

Therapeutic significance:

Understanding the role of Ubiquitin-conjugating enzyme E2 E3 could open doors to potential therapeutic strategies. Its fundamental role in protein ubiquitination and cellular processes makes it a promising target for drug discovery, aiming to modulate its activity for therapeutic benefits.

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