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

Details

Focused On-demand Libraries - Receptor.AI 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.


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 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.


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


 

Fig. 1. The screening workflow of Receptor.AI

It includes comprehensive molecular simulations of the catalytic and allosteric binding pockets and the ensemble virtual screening accounting for their conformational mobility. In the case of designing modulators, the structural changes induced by reaction intermediates are taken into account to leverage 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.

PARTNER
Receptor.AI
 
UPACC
P51170

UPID:
SCNNG_HUMAN

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

BACKGROUND:
Amiloride-sensitive sodium channel subunit gamma, also referred to as Epithelial Na(+) channel subunit gamma or Gamma-NaCH, is a key player in non-voltage-sensitive sodium ion transport across epithelial cell membranes. It is essential for the regulation of sodium and water balance in the body, influencing blood pressure, lung fluid clearance, and sodium reabsorption in the kidney, colon, and sweat glands. This protein's activity is also linked to taste sensation.

THERAPEUTIC SIGNIFICANCE:
The involvement of Amiloride-sensitive sodium channel subunit gamma in diseases such as Liddle syndrome 2, Bronchiectasis with or without elevated sweat chloride 3, and Pseudohypoaldosteronism 1B3, autosomal recessive, highlights its potential as a therapeutic target. The protein's role in these genetic disorders related to electrolyte imbalance and hypertension presents opportunities for developing novel treatments.

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