Focused On-demand Libraries - Receptor.AI 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 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.


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.


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


 

Fig. 1. The screening workflow of Receptor.AI

Utilising molecular simulations, our approach thoroughly examines a wide array of proteins, tracking their conformational changes individually and within complexes. Ensemble virtual screening enables us to address conformational flexibility, revealing essential binding sites at functional regions and allosteric locations. Our rigorous analysis guarantees that no potential mechanism of action is overlooked, aiming to uncover new therapeutic targets and lead compounds across diverse biological functions.


Key features that set our library apart include:


  • The Receptor.AI platform integrates extensive information about the target protein, such as historical experiments, academic research, known ligands, and structural insights, thereby increasing the likelihood of identifying highly relevant compounds.

  • The platform’s sophisticated molecular simulations are designed to discover potential binding sites, ensuring that our focused library is optimal for the discovery of allosteric inhibitors and binders for cryptic pockets.

  • With over 50 customisable AI models, verified through extensive testing in commercial drug discovery and research, Receptor.AI is efficient, reliable, and precise. These models are essential in the production of our focused libraries.

  • Receptor.AI not only produces focused libraries but also provides full services and solutions at every stage of preclinical drug discovery, with a success-based pricing structure that aligns our interests with the success of your project.


PARTNER
Receptor.AI
 
UPACC
Q9ULD8

UPID:
KCNH3_HUMAN

ALTERNATIVE NAMES:
Brain-specific eag-like channel 1; Ether-a-go-go-like potassium channel 2; Voltage-gated potassium channel subunit Kv12.2

ALTERNATIVE UPACC:
Q9ULD8; Q9UQ06

BACKGROUND:
The protein Kv12.2, also referred to as Brain-specific eag-like channel 1 or Ether-a-go-go-like potassium channel 2, plays a vital role in the electrical signaling of neurons. It is the alpha subunit of voltage-gated potassium channels, responsible for generating outward currents with rapid inactivation. The modulation of this channel's properties by cAMP and its assembly with other subunits suggest its significance in neuronal activity regulation.

THERAPEUTIC SIGNIFICANCE:
Exploring the functionalities of Potassium voltage-gated channel subfamily H member 3 offers a pathway to novel therapeutic interventions. Given its essential role in modulating neuronal excitability, targeting this protein could lead to breakthroughs in treating neurological conditions.

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