Focused On-demand Library for N-acetylglucosamine-1-phosphotransferase subunit gamma

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.

We pick out particular compounds from an extensive virtual database of more than 60 billion molecules. The preparation and shipment of these compounds are facilitated by our associate Reaxense.

Contained in the library are leading modulators, each labelled with 38 ADME-Tox and 32 physicochemical and drug-likeness qualities. In addition, each compound is illustrated with its optimal docking poses, affinity scores, and activity scores, giving a complete picture.

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

 Fig. 1. The sreening 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.

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:

GlcNAc-1-phosphotransferase subunit gamma; UDP-N-acetylglucosamine-1-phosphotransferase subunit gamma

Alternative UPACC:

Q9UJJ9; B2R556; Q6XYD7; Q96L13


The N-acetylglucosamine-1-phosphotransferase subunit gamma, also known as GlcNAc-1-phosphotransferase subunit gamma, plays a crucial role in the formation of mannose 6-phosphate (M6P) markers on high mannose type oligosaccharides within the Golgi apparatus. This process is essential for the proper trafficking of lysosomal hydrolases, highlighting the protein's pivotal role in cellular function and health.

Therapeutic significance:

Given its involvement in Mucolipidosis type III complementation group C, a disorder characterized by lysosomal hydrolase trafficking defects leading to severe physical and cognitive impairments, the N-acetylglucosamine-1-phosphotransferase subunit gamma represents a promising target for therapeutic intervention. Understanding its function could pave the way for novel treatments aimed at correcting the underlying genetic and biochemical pathways.

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