Focused On-demand Library for Amiloride-sensitive sodium channel subunit gamma

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.

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.

The library includes a list of the most effective modulators, each annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Furthermore, each compound is shown with its optimal docking poses, affinity scores, and activity scores, offering a detailed summary.

We use our state-of-the-art dedicated workflow for designing focused libraries for enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

It includes in-depth molecular simulations of both the catalytic and allosteric binding pockets, with ensemble virtual screening focusing on their conformational flexibility. For modulators, the process includes considering the structural shifts due to reaction intermediates to boost activity and selectivity.

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:

Epithelial Na(+) channel subunit gamma; Gamma-NaCH; Nonvoltage-gated sodium channel 1 subunit gamma; SCNEG

Alternative UPACC:

P51170; P78437; Q6PCC2; Q93023; Q93024; Q93025; Q93026; Q93027; Q96TD2


The Amiloride-sensitive sodium channel subunit gamma, known alternatively as Epithelial Na(+) channel subunit gamma, Gamma-NaCH, Nonvoltage-gated sodium channel 1 subunit gamma, or SCNEG, plays a pivotal role in the regulation of sodium permeability across epithelial cells. This protein facilitates the electrodiffusion of sodium, crucial for maintaining electrolyte balance, blood pressure homeostasis, and airway surface liquid homeostasis, which is essential for mucus clearance. It is instrumental in sodium reabsorption in various organs including the kidneys, colon, lungs, and sweat glands, and is also involved in taste perception.

Therapeutic significance:

Mutations in the Amiloride-sensitive sodium channel subunit gamma are linked to Liddle syndrome 2, Bronchiectasis with or without elevated sweat chloride 3, and Pseudohypoaldosteronism 1B3, autosomal recessive. These conditions underscore the protein's critical role in fluid and electrolyte homeostasis. Understanding its function and the impact of its genetic variants offers a pathway to targeted treatments for these disorders, highlighting its therapeutic significance.

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