AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Potassium voltage-gated channel subfamily D member 3

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

This comprehensive focused library is produced on demand with state-of-the-art virtual screening and parameter assessment technology driven by Receptor.AI drug discovery platform. This approach outperforms traditional methods and provides higher-quality compounds with superior 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 features a range of promising modulators, each detailed with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Plus, each compound is presented with its ideal docking poses, affinity scores, and activity scores, ensuring a thorough insight.

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

 Fig. 1. The sreening workflow of Receptor.AI

It includes extensive molecular simulations of the channel in its native membrane environment in open, closed and inactivated forms and the ensemble virtual screening accounting for conformational mobility in each of these states. Tentative binding pockets are considered inside the pore, in the gating region and in the allosteric locations to cover the whole spectrum of possible mechanisms of action.

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

Reaxense

upacc

Q9UK17

UPID:

KCND3_HUMAN

Alternative names:

Voltage-gated potassium channel subunit Kv4.3

Alternative UPACC:

Q9UK17; O60576; O60577; Q14D71; Q5T0M0; Q9UH85; Q9UH86; Q9UK16

Background:

Potassium voltage-gated channel subfamily D member 3, also known as Kv4.3, plays a crucial role in the electrical signaling in neurons and the heart. It forms the pore-forming (alpha) subunit of voltage-gated rapidly inactivating A-type potassium channels, contributing to the I(To) current in the heart and I(Sa) current in neurons. Its modulation by interactions with other alpha subunits and regulatory subunits fine-tunes channel properties.

Therapeutic significance:

Kv4.3's involvement in Spinocerebellar ataxia 19 and Brugada syndrome 9 highlights its therapeutic significance. Understanding its role in these diseases could lead to targeted treatments for the cerebellar ataxic syndrome with cognitive impairment and the life-threatening tachyarrhythmia, respectively.

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