Focused On-demand Library for Potassium voltage-gated channel subfamily KQT member 5

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.

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 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.

We utilise our cutting-edge, exclusive workflow to develop focused libraries for enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

It includes comprehensive molecular simulations of the catalytic and allosteric binding pockets and the ensemble virtual screening accounting for their conformational mobility. In the case of designing modulators, the structural changes induced by reaction intermediates are taken into account to leverage activity and selectivity.

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:

KQT-like 5; Potassium channel subunit alpha KvLQT5; Voltage-gated potassium channel subunit Kv7.5

Alternative UPACC:

Q9NR82; A6NKT6; A6PVT6; A8MSQ5; B4DS33; B5MC83; B7ZL37; F5GZV0; Q17RE1; Q5VVP3; Q86W40; Q9NRN0; Q9NYA6


Potassium voltage-gated channel subfamily KQT member 5, also known as KCNQ5, plays a pivotal role in neuronal excitability through its contribution to M-type potassium currents. This channel, in association with KCNQ3, forms a potassium channel critical for modulating the electrical excitability of neurons. It exhibits unique properties, such as insensitivity to tetraethylammonium and inhibition by barium, linopirdine, and XE991, highlighting its distinct pharmacological profile.

Therapeutic significance:

KCNQ5's involvement in Intellectual developmental disorder, autosomal dominant 46, underscores its therapeutic potential. Understanding the role of KCNQ5 could open doors to potential therapeutic strategies, offering hope for targeted interventions in intellectual disability and developmental delay.

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