Focused On-demand Library for Tyrosine-protein phosphatase non-receptor type 9

Focused On-demand Libraries - Receptor.AI 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.


From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Reaxense aids in their synthesis and provision.


The library includes a list of the most promising modulators annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Also, each compound is presented with its optimal docking poses, affinity scores, and activity scores, providing a comprehensive overview.


We utilise our cutting-edge, exclusive workflow to develop focused libraries for enzymes.


 

Fig. 1. The screening workflow of Receptor.AI

It includes comprehensive molecular simulations of the catalytic and allosteric binding pockets and the ensemble virtual screening accounting for their conformational mobility. In the case of designing modulators, the structural changes induced by reaction intermediates are taken into account to leverage activity and selectivity.


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
Receptor.AI
 
UPACC
P43378

UPID:
PTN9_HUMAN

ALTERNATIVE NAMES:
Protein-tyrosine phosphatase MEG2

ALTERNATIVE UPACC:
P43378; Q53XR9

BACKGROUND:
The enzyme Tyrosine-protein phosphatase non-receptor type 9, alternatively named Protein-tyrosine phosphatase MEG2 and identified by the accession number P43378, is crucial for cellular signaling pathways. It acts by removing phosphate groups from tyrosine residues on proteins, thereby modulating their activity. This phosphatase's role extends to the transfer of hydrophobic ligands and participation in Golgi apparatus functions, which are essential for protein sorting and secretion.

THERAPEUTIC SIGNIFICANCE:
The study of Tyrosine-protein phosphatase non-receptor type 9 holds significant promise for the development of new therapeutic approaches. By elucidating its functions and regulatory mechanisms, researchers can identify novel strategies to manipulate its activity, potentially leading to breakthrough treatments for conditions where dysregulated protein phosphorylation is a factor.

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