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 features a range of promising modulators, each detailed with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Plus, each compound is presented with its ideal docking poses, affinity scores, and activity scores, ensuring a thorough insight.


We employ our advanced, specialised process to create targeted 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
P0DPD8

UPID:
EFCE2_HUMAN

ALTERNATIVE NAMES:
-

ALTERNATIVE UPACC:
P0DPD8; A5PLK8; O60344; Q6NTG7; Q6UW36; Q8NFD7; Q96NX3; Q96NX4; Q9BRZ8

BACKGROUND:
EEF1AKMT4-ECE2 readthrough transcript protein is implicated in critical biochemical pathways, including the conversion of big endothelin-1 to endothelin-1 and possibly in amyloid-beta processing. This protein's activity suggests a significant function in maintaining vascular integrity and potentially in modulating processes related to neurodegeneration.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions and mechanisms of the EEF1AKMT4-ECE2 readthrough transcript protein holds promise for unveiling novel therapeutic targets. Its involvement in key physiological processes underscores the potential for developing treatments for cardiovascular and neurodegenerative disorders.

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