Focused On-demand Libraries - Receptor.AI 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 Reaxense.


The library includes a list of the most promising modulators annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Also, each compound is presented with its optimal docking poses, affinity scores, and activity scores, providing a comprehensive overview.


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


 

Fig. 1. The screening 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.


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.


PARTNER
Receptor.AI
 
UPACC
P57789

UPID:
KCNKA_HUMAN

ALTERNATIVE NAMES:
Outward rectifying potassium channel protein TREK-2; TREK-2 K(+) channel subunit

ALTERNATIVE UPACC:
P57789; B2R8T4; B2RCT3; B5TJL4; Q6B014; Q8TDK7; Q8TDK8; Q9HB59

BACKGROUND:
Potassium channel subfamily K member 10, alternatively named Outward rectifying potassium channel protein TREK-2 or TREK-2 K(+) channel subunit, plays a pivotal role in generating rapidly activating and non-inactivating outward rectifier K(+) currents. Its activation by arachidonic acid and unsaturated free fatty acids sets it apart in the potassium channel family.

THERAPEUTIC SIGNIFICANCE:
Exploring the functionalities of Potassium channel subfamily K member 10 unveils promising avenues for therapeutic intervention. Given its critical role in potassium ion transport and cellular excitability, targeting TREK-2 could lead to innovative treatments for diseases where potassium channel regulation is beneficial.

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