Focused On-demand Library for E3 ubiquitin-protein ligase NEDD4-like

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.

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.

The library includes a list of the most effective modulators, each annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Furthermore, each compound is shown with its optimal docking poses, affinity scores, and activity scores, offering a detailed summary.

We use our state-of-the-art dedicated workflow for designing 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 distinguishes itself through several key aspects:

  • The Receptor.AI platform integrates all available data about the target protein, including past experiments, literature data, known ligands, structural information and more. This consolidated approach maximises the probability of prioritising highly relevant compounds.
  • The platform uses sophisticated molecular simulations to identify possible binding sites so that the compounds in the focused library are suitable for discovering allosteric inhibitors and the binders for cryptic pockets.
  • The platform integrates over 50 highly customisable AI models, which are thoroughly tested and validated on a multitude of commercial drug discovery programs and research projects. It is designed to be efficient, reliable and accurate. All this power is utilised when producing the focused libraries.
  • In addition to producing the focused libraries, Receptor.AI provides services and end-to-end solutions at every stage of preclinical drug discovery. The pricing model is success-based, which reduces your risks and leverages the mutual benefits of the project's success.







Alternative names:

HECT-type E3 ubiquitin transferase NED4L; NEDD4.2; Nedd4-2

Alternative UPACC:

Q96PU5; O43165; Q3LSM7; Q7Z5F1; Q7Z5F2; Q7Z5N3; Q8N5A7; Q8WUU9; Q9BW58; Q9H2W4; Q9NT88


E3 ubiquitin-protein ligase NEDD4-like, also known as NEDD4.2 or Nedd4-2, plays a pivotal role in various cellular processes through the polyubiquitination of target proteins. This enzyme is involved in autophagy, innate immunity, DNA repair, and the regulation of signaling pathways such as TGF-beta. It also influences the ubiquitination and internalization of plasma membrane channels and the degradation of specific proteins, contributing to cellular homeostasis and signaling.

Therapeutic significance:

The involvement of E3 ubiquitin-protein ligase NEDD4-like in Periventricular nodular heterotopia 7, characterized by developmental delays, intellectual disability, and seizures, underscores its potential as a therapeutic target. Understanding the role of this protein could open doors to potential therapeutic strategies for treating neurological disorders and improving patient outcomes.

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