AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Sodium channel subunit beta-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.

We carefully select specific compounds from a vast collection of over 60 billion molecules in virtual chemical space. Our partner Reaxense helps in synthesizing and delivering these compounds.

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 utilise our cutting-edge, exclusive workflow to develop focused libraries for ion channels.

 Fig. 1. The sreening workflow of Receptor.AI

The method involves in-depth molecular simulations of the ion channel in its native membrane environment, including its open, closed, and inactivated states, along with ensemble virtual screening that focuses on conformational mobility for each state. Tentative binding pockets are identified inside the pore, in the gating area, and at allosteric sites to address every conceivable mechanism of action.

Our library stands out due to several important features:

  • The Receptor.AI platform compiles comprehensive data on the target protein, encompassing previous experiments, literature, known ligands, structural details, and more, leading to a higher chance of selecting the most relevant compounds.
  • Advanced molecular simulations on the platform help pinpoint potential binding sites, making the compounds in our focused library ideal for finding allosteric inhibitors and targeting cryptic pockets.
  • Receptor.AI boasts over 50 tailor-made AI models, rigorously tested and proven in various drug discovery projects and research initiatives. They are crafted for efficacy, dependability, and precision, all of which are key in creating our focused libraries.
  • Beyond creating focused libraries, Receptor.AI offers comprehensive services and complete solutions throughout the preclinical drug discovery phase. Our success-based pricing model minimises risk and maximises the mutual benefits of the project's success.

partner

Reaxense

upacc

Q9NY72

UPID:

SCN3B_HUMAN

Alternative names:

-

Alternative UPACC:

Q9NY72; A5H1I5; Q17RL3; Q9ULR2

Background:

Sodium channel subunit beta-3 plays a crucial role in modulating channel gating kinetics and causes unique persistent sodium currents. It inactivates the sodium channel opening more slowly than the subunit beta-1 and is associated with NFASC, targeting sodium channels to the nodes of Ranvier in developing axons and retaining these channels in mature myelinated axons.

Therapeutic significance:

Sodium channel subunit beta-3 is implicated in Brugada syndrome 7 and familial atrial fibrillation 16. These conditions highlight the protein's critical role in cardiac rhythm disturbances, offering a promising target for therapeutic intervention to manage and potentially cure these life-threatening diseases.

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