Focused On-demand Library for N-terminal EF-hand calcium-binding protein 3

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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.

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

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.

Our high-tech, dedicated method is applied to construct targeted libraries.

 Fig. 1. The sreening 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.

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:

Amyloid-beta A4 protein-binding family A member 2-binding protein; Nek2-interacting protein 1; Neuronal calcium-binding protein 3; X11L-binding protein 51

Alternative UPACC:

Q96P71; A8K780; E1P5N2; Q5JWF5; Q5JWF6; Q5JWF7; Q86VV1; Q9H433; Q9H8G8; Q9HBW7; Q9HCQ9


N-terminal EF-hand calcium-binding protein 3, also known as Amyloid-beta A4 protein-binding family A member 2-binding protein, plays a crucial role in inhibiting the interaction of APBA2 with amyloid-beta precursor protein (APP), facilitating the formation of amyloid-beta. It is also implicated in enhancing the activity of HIF1A, promoting glycolysis under normoxic conditions through its ABM domain. This function may involve the stabilization of the interaction between HIF1AN and APBA3.

Therapeutic significance:

Understanding the role of N-terminal EF-hand calcium-binding protein 3 could open doors to potential therapeutic strategies.

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