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 carefully select specific compounds from a vast collection of over 60 billion molecules in virtual chemical space. Reaxense helps in synthesizing and delivering these compounds.


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 top-notch dedicated system is used to design specialised libraries.


 

Fig. 1. The screening workflow of Receptor.AI

Our methodology employs molecular simulations to explore a wide array of proteins, capturing their dynamic states both individually and within complexes. Through ensemble virtual screening, we address conformational mobility, uncovering binding sites within functional regions and remote allosteric locations. This thorough exploration ensures no potential mechanism of action is overlooked, aiming to discover novel therapeutic targets and lead compounds across an extensive spectrum of biological functions.


Our library stands out due to several important features:


  • The Receptor.AI platform compiles comprehensive data on the target protein, encompassing previous experiments, literature, known ligands, structural details, and more, leading to a higher chance of selecting the most relevant compounds.

  • Advanced molecular simulations on the platform help pinpoint potential binding sites, making the compounds in our focused library ideal for finding allosteric inhibitors and targeting cryptic pockets.

  • Receptor.AI boasts over 50 tailor-made AI models, rigorously tested and proven in various drug discovery projects and research initiatives. They are crafted for efficacy, dependability, and precision, all of which are key in creating our focused libraries.

  • Beyond creating focused libraries, Receptor.AI offers comprehensive services and complete solutions throughout the preclinical drug discovery phase. Our success-based pricing model minimises risk and maximises the mutual benefits of the project's success.


PARTNER
Receptor.AI
 
UPACC
Q86W47

UPID:
KCMB4_HUMAN

ALTERNATIVE NAMES:
BK channel subunit beta-4; Calcium-activated potassium channel, subfamily M subunit beta-4; Charybdotoxin receptor subunit beta-4; K(VCA)beta-4; Maxi K channel subunit beta-4; Slo-beta-4

ALTERNATIVE UPACC:
Q86W47; Q8IVR3; Q9NPA4; Q9P0G5

BACKGROUND:
Calcium-activated potassium channel subunit beta-4, also referred to as Slo-beta-4 and Charybdotoxin receptor subunit beta-4, is instrumental in diversifying the KCNMA1 channel's function. It achieves this by decreasing the gating kinetics and calcium sensitivity of the channel at low calcium concentrations, while increasing channel openings at higher concentrations, thereby making the channel resistant to certain toxin concentrations.

THERAPEUTIC SIGNIFICANCE:
Exploring the functionalities of Calcium-activated potassium channel subunit beta-4 presents a promising avenue for the development of novel therapeutic approaches.

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