Focused On-demand Library for NF-kappa-B inhibitor-interacting Ras-like protein 2

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.

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

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.

We employ our advanced, specialised process to create targeted libraries.

 Fig. 1. The sreening workflow of Receptor.AI

Utilising molecular simulations, our approach thoroughly examines a wide array of proteins, tracking their conformational changes individually and within complexes. Ensemble virtual screening enables us to address conformational flexibility, revealing essential binding sites at functional regions and allosteric locations. Our rigorous analysis guarantees that no potential mechanism of action is overlooked, aiming to uncover new therapeutic targets and lead compounds across diverse 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:

I-kappa-B-interacting Ras-like protein 2

Alternative UPACC:

Q9NYR9; A6NCZ5; B3KNN0; B4DNM3; Q6PK52; Q96KC7; Q9NSX1


NF-kappa-B inhibitor-interacting Ras-like protein 2, also known as I-kappa-B-interacting Ras-like protein 2, plays a crucial role in regulating NF-kappa-B activity. It achieves this by preventing the degradation of NF-kappa-B inhibitor beta (NFKBIB), thus making NFKBIB more resistant to degradation. This protein may function by inhibiting the phosphorylation of NFKBIB and the nuclear localization of the p65/RELA NF-kappa-B subunit. Its mechanism of action, whether as a GTPase, remains to be fully elucidated, with both GTP- and GDP-bound forms inhibiting NFKBIB phosphorylation.

Therapeutic significance:

Understanding the role of NF-kappa-B inhibitor-interacting Ras-like protein 2 could open doors to potential therapeutic strategies.

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