Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

The specialised, focused library is developed on demand with the most recent virtual screening and parameter assessment technology, guided by the Receptor.AI drug discovery platform. This approach exceeds the capabilities of traditional methods and offers compounds with higher activity, selectivity, and safety.


The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is facilitated 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.


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


 

Fig. 1. The screening workflow of Receptor.AI

Our methodology employs molecular simulations to explore a wide array of proteins, capturing their dynamic states both individually and within complexes. Through ensemble virtual screening, we address conformational mobility, uncovering binding sites within functional regions and remote allosteric locations. This thorough exploration ensures no potential mechanism of action is overlooked, aiming to discover novel therapeutic targets and lead compounds across an extensive 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
Q9UJJ9

UPID:
GNPTG_HUMAN

ALTERNATIVE NAMES:
GlcNAc-1-phosphotransferase subunit gamma; UDP-N-acetylglucosamine-1-phosphotransferase subunit gamma

ALTERNATIVE UPACC:
Q9UJJ9; B2R556; Q6XYD7; Q96L13

BACKGROUND:
The UDP-N-acetylglucosamine-1-phosphotransferase subunit gamma functions as a non-catalytic component of the enzyme complex responsible for tagging lysosomal enzymes with mannose 6-phosphate markers. This tagging is critical for the enzymes' lysosomal targeting, underscoring the protein's essential role in maintaining cellular homeostasis and processing biological molecules.

THERAPEUTIC SIGNIFICANCE:
Understanding the role of N-acetylglucosamine-1-phosphotransferase subunit gamma could open doors to potential therapeutic strategies. Its direct link to Mucolipidosis type III complementation group C, a disease marked by significant musculoskeletal and developmental challenges, underscores the importance of targeted research and drug development efforts aimed at mitigating the effects of this and related lysosomal storage disorders.

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