AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Cell growth-regulating nucleolar protein

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

This extensive focused library is tailor-made using the latest virtual screening and parameter assessment technology, operated by the Receptor.AI drug discovery platform. This technique is more effective than traditional methods, offering compounds with improved 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 our partner Reaxense.

The library features a range of promising modulators, each detailed with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Plus, each compound is presented with its ideal docking poses, affinity scores, and activity scores, ensuring a thorough insight.

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

 Fig. 1. The sreening workflow of Receptor.AI

Utilising molecular simulations, our approach thoroughly examines a wide array of proteins, tracking their conformational changes individually and within complexes. Ensemble virtual screening enables us to address conformational flexibility, revealing essential binding sites at functional regions and allosteric locations. Our rigorous analysis guarantees that no potential mechanism of action is overlooked, aiming to uncover new therapeutic targets and lead compounds across diverse biological functions.

Key features that set our library apart include:

  • The Receptor.AI platform integrates extensive information about the target protein, such as historical experiments, academic research, known ligands, and structural insights, thereby increasing the likelihood of identifying highly relevant compounds.
  • The platform’s sophisticated molecular simulations are designed to discover potential binding sites, ensuring that our focused library is optimal for the discovery of allosteric inhibitors and binders for cryptic pockets.
  • With over 50 customisable AI models, verified through extensive testing in commercial drug discovery and research, Receptor.AI is efficient, reliable, and precise. These models are essential in the production of our focused libraries.
  • Receptor.AI not only produces focused libraries but also provides full services and solutions at every stage of preclinical drug discovery, with a success-based pricing structure that aligns our interests with the success of your project.

partner

Reaxense

upacc

Q9NX58

UPID:

LYAR_HUMAN

Alternative names:

-

Alternative UPACC:

Q9NX58; D3DVS4; Q6FI78; Q9NYS1

Background:

The Cell growth-regulating nucleolar protein, identified by the accession number Q9NX58, is pivotal in rRNA processing, transcription regulation, and innate immune response modulation. It plays a crucial role in converting 47S/45S pre-rRNA to 32S/30S pre-rRNAs, leading to the production of 18S and 28S rRNAs. This protein also represses the expression of the gamma-globin promoter and oxidative stress genes, binds to specific DNA motifs, and negatively regulates antiviral responses and pro-inflammatory cytokines expression.

Therapeutic significance:

Understanding the role of Cell growth-regulating nucleolar protein could open doors to potential therapeutic strategies.

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