Focused On-demand Libraries - Receptor.AI 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. 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 screening workflow of Receptor.AI

This approach involves comprehensive molecular simulations of the catalytic and allosteric binding pockets and ensemble virtual screening that accounts for their conformational flexibility. In the case of designing modulators, the structural adjustments caused by reaction intermediates are considered to improve 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
Q13469

UPID:
NFAC2_HUMAN

ALTERNATIVE NAMES:
NFAT pre-existing subunit; T-cell transcription factor NFAT1

ALTERNATIVE UPACC:
Q13469; B5B2N8; B5B2N9; B5B2P0; B5B2P2; B5B2P3; Q13468; Q5TFW7; Q5TFW8; Q9NPX6; Q9NQH3; Q9UJR2

BACKGROUND:
The protein Nuclear factor of activated T-cells, cytoplasmic 2, with alternative names NFAT pre-existing subunit and T-cell transcription factor NFAT1, is crucial for cytokine gene expression in T-cells and plays roles in cell migration and chondrogenesis. Its activities are essential for the proper functioning of the immune system and bone development.

THERAPEUTIC SIGNIFICANCE:
Given NFATC2's critical role in diseases such as joint contractures, osteochondromas, and B-cell lymphoma, targeting this protein could lead to innovative treatments. Its multifaceted role in biological systems makes it an intriguing subject for scientific inquiry and drug development.

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