Focused On-demand Library for Potassium voltage-gated channel subfamily KQT member 2

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

The specialised, focused library is developed on demand with the most recent virtual screening and parameter assessment technology, guided by the Receptor.AI drug discovery platform. This approach exceeds the capabilities of traditional methods and offers compounds with higher 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 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 sreening workflow of Receptor.AI

The procedure entails thorough molecular simulations of the catalytic and allosteric binding pockets, accompanied by ensemble virtual screening that factors in their conformational flexibility. When developing modulators, the structural modifications brought about by reaction intermediates are factored in to optimize activity and selectivity.

Several key aspects differentiate our library:

  • Receptor.AI compiles an all-encompassing dataset on the target protein, including historical experiments, literature data, known ligands, and structural insights, maximising the chances of prioritising the most pertinent compounds.
  • The platform employs state-of-the-art molecular simulations to identify potential binding sites, ensuring the focused library is primed for discovering allosteric inhibitors and binders of concealed pockets.
  • Over 50 customisable AI models, thoroughly evaluated in various drug discovery endeavours and research projects, make Receptor.AI both efficient and accurate. This technology is integral to the development of our focused libraries.
  • In addition to generating focused libraries, Receptor.AI offers a full range of services and solutions for every step of preclinical drug discovery, with a pricing model based on success, thereby reducing risk and promoting joint project success.







Alternative names:

KQT-like 2; Neuroblastoma-specific potassium channel subunit alpha KvLQT2; Voltage-gated potassium channel subunit Kv7.2

Alternative UPACC:

O43526; O43796; O75580; O95845; Q4VXP4; Q4VXR6; Q5VYT8; Q96J59; Q99454


Potassium voltage-gated channel subfamily KQT member 2 (Kv7.2), also known as KQT-like 2 or Neuroblastoma-specific potassium channel subunit alpha KvLQT2, plays a pivotal role in neuronal excitability. It forms a potassium channel with KCNQ3, crucial for the M-current that regulates neurons' response to stimuli. This channel's activity is influenced by various compounds, including linopirdine and retigabine.

Therapeutic significance:

Kv7.2 is implicated in severe neurological disorders, such as benign familial neonatal seizures 1 and developmental and epileptic encephalopathy 7, characterized by early-life seizures and potential developmental delays. Understanding Kv7.2's role could lead to novel treatments for these conditions.

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