Focused On-demand Library for CDGSH iron-sulfur domain-containing protein 2

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.

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.

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.

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

 Fig. 1. The sreening workflow of Receptor.AI

By deploying molecular simulations, our approach comprehensively covers a broad array of proteins, tracking their flexibility and dynamics individually and within complexes. Ensemble virtual screening is utilised to take into account conformational dynamics, identifying pivotal binding sites located within functional regions and at allosteric locations. This thorough exploration ensures that every conceivable mechanism of action is considered, aiming to identify new therapeutic targets and advance lead compounds throughout a vast spectrum of biological functions.

Our library distinguishes itself through several key aspects:

  • The Receptor.AI platform integrates all available data about the target protein, including past experiments, literature data, known ligands, structural information and more. This consolidated approach maximises the probability of prioritising highly relevant compounds.
  • The platform uses sophisticated molecular simulations to identify possible binding sites so that the compounds in the focused library are suitable for discovering allosteric inhibitors and the binders for cryptic pockets.
  • The platform integrates over 50 highly customisable AI models, which are thoroughly tested and validated on a multitude of commercial drug discovery programs and research projects. It is designed to be efficient, reliable and accurate. All this power is utilised when producing the focused libraries.
  • In addition to producing the focused libraries, Receptor.AI provides services and end-to-end solutions at every stage of preclinical drug discovery. The pricing model is success-based, which reduces your risks and leverages the mutual benefits of the project's success.







Alternative names:

Endoplasmic reticulum intermembrane small protein; MitoNEET-related 1 protein; Nutrient-deprivation autophagy factor-1

Alternative UPACC:

Q8N5K1; Q7Z3D5


CDGSH iron-sulfur domain-containing protein 2, also known as Endoplasmic reticulum intermembrane small protein, MitoNEET-related 1 protein, and Nutrient-deprivation autophagy factor-1, plays a pivotal role in cellular autophagy regulation. It interacts with BCL2 and BECN1 to modulate endoplasmic reticulum Ca(2+) levels during autophagy and is crucial for BIK-initiated autophagy, influencing life span control.

Therapeutic significance:

Given its involvement in Wolfram syndrome 2, a condition marked by insulin-dependent diabetes mellitus, optic atrophy, and other severe manifestations, understanding the role of CDGSH iron-sulfur domain-containing protein 2 could open doors to potential therapeutic strategies for this rare disorder.

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