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.


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 use our state-of-the-art dedicated workflow for designing 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.


Our library distinguishes itself through several key aspects:


  • The Receptor.AI platform integrates all available data about the target protein, including past experiments, literature data, known ligands, structural information and more. This consolidated approach maximises the probability of prioritising highly relevant compounds.

  • The platform uses sophisticated molecular simulations to identify possible binding sites so that the compounds in the focused library are suitable for discovering allosteric inhibitors and the binders for cryptic pockets.

  • The platform integrates over 50 highly customisable AI models, which are thoroughly tested and validated on a multitude of commercial drug discovery programs and research projects. It is designed to be efficient, reliable and accurate. All this power is utilised when producing the focused libraries.

  • In addition to producing the focused libraries, Receptor.AI provides services and end-to-end solutions at every stage of preclinical drug discovery. The pricing model is success-based, which reduces your risks and leverages the mutual benefits of the project's success.


PARTNER
Receptor.AI
 
UPACC
Q9NR56

UPID:
MBNL1_HUMAN

ALTERNATIVE NAMES:
Triplet-expansion RNA-binding protein

ALTERNATIVE UPACC:
Q9NR56; E9PBW7; O43311; O43797; Q86UV8; Q86UV9; Q96P92; Q96RE3

BACKGROUND:
The Muscleblind-like protein 1, recognized for its alternative names such as Triplet-expansion RNA-binding protein, is central to the regulation of alternative splicing of pre-mRNA. This regulation is crucial for proper muscle and cardiac development and function. The protein's specific binding to RNA sequences and its involvement in spliceosome assembly demonstrate its essential role in cellular biology.

THERAPEUTIC SIGNIFICANCE:
Understanding the role of Muscleblind-like protein 1 could open doors to potential therapeutic strategies for diseases like Dystrophia myotonica 1 and Fuchs endothelial corneal dystrophy, 3. Its involvement in disease mechanisms through RNA sequestration and missplicing presents a promising target for therapeutic intervention.

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