Focused On-demand Library for Nuclear factor of activated T-cells, cytoplasmic 2

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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.

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.

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 procedure entails thorough molecular simulations of the catalytic and allosteric binding pockets, accompanied by ensemble virtual screening that factors in their conformational flexibility. When developing modulators, the structural modifications brought about by reaction intermediates are factored in to optimize 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.







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


Nuclear factor of activated T-cells, cytoplasmic 2 (NFATC2), also known as NFAT pre-existing subunit and T-cell transcription factor NFAT1, plays a pivotal role in T-cell activation and the inducible expression of cytokine genes, including IL-2, IL-3, IL-4, TNF-alpha, and GM-CSF. It also promotes invasive migration through GPC6 expression and the WNT5A signaling pathway, and negatively regulates chondrogenesis.

Therapeutic significance:

NFATC2's involvement in joint contractures, osteochondromas, and B-cell lymphoma highlights its potential as a therapeutic target. Understanding the role of NFATC2 could open doors to potential therapeutic strategies for these conditions.

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