Focused On-demand Library for RuvB-like 2

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.


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


 

Fig. 1. The screening 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 stands out due to several important features:


  • The Receptor.AI platform compiles comprehensive data on the target protein, encompassing previous experiments, literature, known ligands, structural details, and more, leading to a higher chance of selecting the most relevant compounds.

  • Advanced molecular simulations on the platform help pinpoint potential binding sites, making the compounds in our focused library ideal for finding allosteric inhibitors and targeting cryptic pockets.

  • Receptor.AI boasts over 50 tailor-made AI models, rigorously tested and proven in various drug discovery projects and research initiatives. They are crafted for efficacy, dependability, and precision, all of which are key in creating our focused libraries.

  • Beyond creating focused libraries, Receptor.AI offers comprehensive services and complete solutions throughout the preclinical drug discovery phase. Our success-based pricing model minimises risk and maximises the mutual benefits of the project's success.

PARTNER
Receptor.AI
 
UPACC
Q9Y230

UPID:
RUVB2_HUMAN

ALTERNATIVE NAMES:
48 kDa TATA box-binding protein-interacting protein; 51 kDa erythrocyte cytosolic protein; INO80 complex subunit J; Repressing pontin 52; TIP49b; TIP60-associated protein 54-beta

ALTERNATIVE UPACC:
Q9Y230; B3KQ59; E7ETE5; Q6FIB9; Q6PK27; Q9Y361

BACKGROUND:
RuvB-like 2, also referred to as Repressing pontin 52 and TIP60-associated protein 54-beta, is integral to the chromatin remodeling INO80 complex, contributing to its DNA- and nucleosome-activated ATPase activity. This protein is crucial for oncogenic transformation by MYC, transcriptional activation by the LEF1/TCF1-CTNNB1 complex, and plays a role in the endoplasmic reticulum-associated degradation pathway, modulating stress response gene expression.

THERAPEUTIC SIGNIFICANCE:
Understanding the role of RuvB-like 2 could open doors to potential therapeutic strategies.

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