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.


The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is facilitated by Reaxense.


Contained in the library are leading modulators, each labelled with 38 ADME-Tox and 32 physicochemical and drug-likeness qualities. In addition, each compound is illustrated with its optimal docking poses, affinity scores, and activity scores, giving a complete picture.


Our top-notch dedicated system is used to design specialised libraries for ion channels.


 

Fig. 1. The screening 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 is unique due to several crucial aspects:


  • Receptor.AI compiles all relevant data on the target protein, such as past experimental results, literature findings, known ligands, and structural data, thereby enhancing the likelihood of focusing on the most significant compounds.

  • By utilizing advanced molecular simulations, the platform is adept at locating potential binding sites, rendering the compounds in the focused library well-suited for unearthing allosteric inhibitors and binders for hidden pockets.

  • The platform is supported by more than 50 highly specialized AI models, all of which have been rigorously tested and validated in diverse drug discovery and research programs. Its design emphasizes efficiency, reliability, and accuracy, crucial for producing focused libraries.

  • Receptor.AI extends beyond just creating focused libraries; it offers a complete spectrum of services and solutions during the preclinical drug discovery phase, with a success-dependent pricing strategy that reduces risk and fosters shared success in the project.


PARTNER
Receptor.AI
 
UPACC
O95069

UPID:
KCNK2_HUMAN

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

BACKGROUND:
The protein TREK-1, encoded by the KCNK2 gene, is integral to passive transmembrane potassium transport. It uniquely transitions between two functional states influenced by phosphorylation. TREK-1's ability to form heterodimeric potassium channels with KCNK1 in astrocytes is essential for the rapid release of glutamate, a critical neurotransmitter, highlighting its significant role in neuronal communication.

THERAPEUTIC SIGNIFICANCE:
Exploring the functionalities of TREK-1, particularly its regulatory mechanisms in potassium channel activity, could unveil novel therapeutic avenues. Given its critical role in neurotransmitter release and neuronal excitability, TREK-1 represents a promising target for developing treatments for neurological disorders.

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