Focused On-demand Libraries - Receptor.AI 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.


Our selection of compounds is from a large virtual library of over 60 billion molecules. The production and distribution of these compounds are managed by Reaxense.


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 screening workflow of Receptor.AI

It includes in-depth molecular simulations of both the catalytic and allosteric binding pockets, with ensemble virtual screening focusing on their conformational flexibility. For modulators, the process includes considering the structural shifts due to reaction intermediates to boost 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.


PARTNER
Receptor.AI
 
UPACC
O00308

UPID:
WWP2_HUMAN

ALTERNATIVE NAMES:
Atrophin-1-interacting protein 2; HECT-type E3 ubiquitin transferase WWP2; WW domain-containing protein 2

ALTERNATIVE UPACC:
O00308; A6NEP1; B2R706; B4DTL5; F5H213; H3BRF3; I3RSG8; Q6ZTQ5; Q96CZ2; Q9BWN6

BACKGROUND:
The protein NEDD4-like E3 ubiquitin-protein ligase WWP2, known for its alternative names Atrophin-1-interacting protein 2 and HECT-type E3 ubiquitin transferase WWP2, is pivotal in the ubiquitin-proteasome system. By transferring ubiquitin from E2 enzymes to specific substrates, WWP2 regulates protein levels and activity, impacting embryonic stem cell development, T-cell survival, and metal ion transport.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of NEDD4-like E3 ubiquitin-protein ligase WWP2 unveils potential avenues for therapeutic intervention. Its critical role in protein ubiquitination processes suggests opportunities for targeting WWP2 in therapeutic strategies aimed at correcting protein misregulation.

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