Focused On-demand Library for Protein O-GlcNAcase

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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.

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.

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.

Our top-notch dedicated system is used to design specialised 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.

Several key aspects differentiate our library:

  • Receptor.AI compiles an all-encompassing dataset on the target protein, including historical experiments, literature data, known ligands, and structural insights, maximising the chances of prioritising the most pertinent compounds.
  • The platform employs state-of-the-art molecular simulations to identify potential binding sites, ensuring the focused library is primed for discovering allosteric inhibitors and binders of concealed pockets.
  • Over 50 customisable AI models, thoroughly evaluated in various drug discovery endeavours and research projects, make Receptor.AI both efficient and accurate. This technology is integral to the development of our focused libraries.
  • In addition to generating focused libraries, Receptor.AI offers a full range of services and solutions for every step of preclinical drug discovery, with a pricing model based on success, thereby reducing risk and promoting joint project success.







Alternative names:

Beta-N-acetylglucosaminidase; Beta-N-acetylhexosaminidase; Beta-hexosaminidase; Meningioma-expressed antigen 5; N-acetyl-beta-D-glucosaminidase; N-acetyl-beta-glucosaminidase; Nuclear cytoplasmic O-GlcNAcase and acetyltransferase

Alternative UPACC:

O60502; B7WPB9; D3DR79; E9PGF9; O75166; Q86WV0; Q8IV98; Q9BVA5; Q9HAR0


Protein O-GlcNAcase, also known as Beta-N-acetylglucosaminidase, plays a crucial role in the cleavage of GlcNAc from O-glycosylated proteins. This enzyme exhibits specificity by targeting p-nitrophenyl-beta-GlcNAc and 4-methylumbelliferone-GlcNAc as substrates, showcasing its unique enzymatic activity. It is distinguished by its inability to cleave GalNAc or bind acetyl-CoA, highlighting its specialized function in cellular processes.

Therapeutic significance:

Understanding the role of Protein O-GlcNAcase could open doors to potential therapeutic strategies. Its specific enzymatic activity in processing O-glycosylated proteins underlines its importance in cellular mechanisms, suggesting its potential as a target in therapeutic interventions.

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