Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

The specialised, focused library is developed on demand with the most recent virtual screening and parameter assessment technology, guided by the Receptor.AI drug discovery platform. This approach exceeds the capabilities of traditional methods and offers compounds with higher 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 promising modulators annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Also, each compound is presented with its optimal docking poses, affinity scores, and activity scores, providing a comprehensive overview.


We employ our advanced, specialised process to create targeted 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
O95319

UPID:
CELF2_HUMAN

ALTERNATIVE NAMES:
Bruno-like protein 3; CUG triplet repeat RNA-binding protein 2; CUG-BP- and ETR-3-like factor 2; ELAV-type RNA-binding protein 3; Neuroblastoma apoptosis-related RNA-binding protein; RNA-binding protein BRUNOL-3

ALTERNATIVE UPACC:
O95319; B7ZAN9; Q7KYU4; Q8N499; Q92950; Q96NW9; Q96RQ5; Q96RQ6; Q9UL67

BACKGROUND:
CUGBP Elav-like family member 2, or CUG triplet repeat RNA-binding protein 2, is crucial for RNA-binding, influencing pre-mRNA splicing and mRNA translation. It regulates exon inclusion/exclusion in tissue-specific and developmentally regulated alternative splicing, playing a significant role in skeletal muscle development and neuronal functions.

THERAPEUTIC SIGNIFICANCE:
Given its association with Developmental and epileptic encephalopathy 97, exploring CUGBP Elav-like family member 2's function offers a promising pathway for developing targeted treatments for this debilitating neurological disorder.

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