AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Potassium voltage-gated channel subfamily D member 1

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.

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 our associate 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 employ our advanced, specialised process to create targeted libraries for ion channels.

 Fig. 1. The sreening workflow of Receptor.AI

This process includes comprehensive molecular simulations of the ion channel in its native membrane environment, depicting its open, closed, and inactivated states, and ensemble virtual screening that accounts for conformational mobility in each state. Tentative binding pockets are investigated inside the pore, at the gating region, and in allosteric sites to cover the full 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

Q9NSA2

UPID:

KCND1_HUMAN

Alternative names:

Voltage-gated potassium channel subunit Kv4.1

Alternative UPACC:

Q9NSA2; A6NEF1; B2RCG0; O75671

Background:

Potassium voltage-gated channel subfamily D member 1, also known as Kv4.1, plays a crucial role in the electrical signaling of neurons and cardiac cells. 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 activity is finely tuned through interactions with other alpha subunits and regulatory subunits.

Therapeutic significance:

Understanding the role of Potassium voltage-gated channel subfamily D member 1 could open doors to potential therapeutic strategies.

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