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.


From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Reaxense aids in their synthesis and provision.


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 high-tech, dedicated method is applied to construct targeted libraries for ion channels.


 

Fig. 1. The screening workflow of Receptor.AI

The method involves in-depth molecular simulations of the ion channel in its native membrane environment, including its open, closed, and inactivated states, along with ensemble virtual screening that focuses on conformational mobility for each state. Tentative binding pockets are identified inside the pore, in the gating area, and at allosteric sites to address every conceivable mechanism of action.


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
P51787

UPID:
KCNQ1_HUMAN

ALTERNATIVE NAMES:
IKs producing slow voltage-gated potassium channel subunit alpha KvLQT1; KQT-like 1; Voltage-gated potassium channel subunit Kv7.1

ALTERNATIVE UPACC:
P51787; O00347; O60607; O94787; Q14D14; Q7Z6G9; Q92960; Q9UMN8; Q9UMN9

BACKGROUND:
The protein Kv7.1, also known as KCNQ1, plays a crucial role in cardiac repolarization, auditory functions, and maintaining electrolyte homeostasis. By forming complexes with various KCNE subunits, it regulates the kinetics of potassium-selective outward currents, which are vital for heart rhythm stability and efficient organ function. Its ability to adapt its function through different subunit associations makes it a key player in cellular physiology.

THERAPEUTIC SIGNIFICANCE:
Disruptions in Kv7.1 function are implicated in several cardiac disorders, including Long QT syndrome 1 and Short QT syndrome 2, as well as metabolic diseases like Type 2 diabetes mellitus. These associations underscore the therapeutic potential of targeting Kv7.1 in drug development, aiming to correct or mitigate the effects of these diseases. The exploration of Kv7.1's role in disease mechanisms holds promise for novel treatment strategies.

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