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


 

Fig. 1. The screening workflow of Receptor.AI

Our methodology leverages molecular simulations to examine a vast array of proteins, capturing their dynamics in both isolated forms and in complexes with other proteins. Through ensemble virtual screening, we thoroughly account for the protein's conformational mobility, identifying critical binding sites within functional regions and distant allosteric locations. This detailed exploration ensures that we comprehensively assess every possible mechanism of action, with the objective of identifying novel therapeutic targets and lead compounds that span a wide spectrum of biological functions.


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.


PARTNER
Receptor.AI
 
UPACC
Q9UBF6

UPID:
RBX2_HUMAN

ALTERNATIVE NAMES:
CKII beta-binding protein 1; RING finger protein 7; Regulator of cullins 2; Sensitive to apoptosis gene protein

ALTERNATIVE UPACC:
Q9UBF6; A8K1H9; A8MTB5; C9JYL3; D3DNF7; D3DNF8; Q9BXN8; Q9Y5M7

BACKGROUND:
The protein RING-box protein 2, with alternative names such as CKII beta-binding protein 1 and Regulator of cullins 2, is a probable component of the SCF E3 ubiquitin ligase complex. It mediates crucial processes like ubiquitination, proteasomal degradation of target proteins, and plays a role in cell survival mechanisms. Its interaction with UBE2F promotes neddylation of CUL5, highlighting its significance in cellular processes.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of RING-box protein 2 offers a promising avenue for developing novel therapeutic approaches.

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