Focused On-demand Library for Potassium channel subfamily K member 2

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.

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

This includes extensive molecular simulations of the ion channel in its native membrane environment, in open, closed, and inactivated forms, paired with ensemble virtual screening that factors in conformational mobility in each state. Tentative binding pockets are considered in the pore, the gating region, and allosteric areas to capture the full range of 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.







Alternative names:

Outward rectifying potassium channel protein TREK-1; TREK-1 K(+) channel subunit; Two pore domain potassium channel TREK-1; Two pore potassium channel TPKC1

Alternative UPACC:

O95069; A1Z1V3; A8K618; B2RCS4; B7ZL56; D3DTA5; Q5DP47; Q5DP48; Q9NRT2; Q9UNE3


Potassium channel subfamily K member 2 (KCNK2), also known as TREK-1, plays a pivotal role in potassium transport across the cell membrane. It operates in a phosphorylation-dependent manner, switching between a voltage-insensitive potassium leak channel and a voltage-dependent outward rectifying potassium channel. In astrocytes, KCNK2 predominantly forms heterodimeric channels with KCNK1, crucial for rapid glutamate release upon activation of specific G-protein coupled receptors.

Therapeutic significance:

Understanding the role of Potassium channel subfamily K member 2 could open doors to potential therapeutic strategies. Its involvement in potassium transport and astrocyte function suggests its potential impact on neurological conditions and emphasizes the importance of further research into its mechanisms and regulatory pathways.

Looking for more information on this library or underlying technology? Fill out the form below and we'll be in touch with all the details you need.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.