Focused On-demand Library for ELMO domain-containing protein 3

Details

Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

This comprehensive focused library is produced on demand with state-of-the-art virtual screening and parameter assessment technology driven by Receptor.AI drug discovery platform. This approach outperforms traditional methods and provides higher-quality compounds with superior 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.


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


 

Fig. 1. The screening workflow of Receptor.AI

By deploying molecular simulations, our approach comprehensively covers a broad array of proteins, tracking their flexibility and dynamics individually and within complexes. Ensemble virtual screening is utilised to take into account conformational dynamics, identifying pivotal binding sites located within functional regions and at allosteric locations. This thorough exploration ensures that every conceivable mechanism of action is considered, aiming to identify new therapeutic targets and advance lead compounds throughout a vast spectrum of 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
Q96FG2

UPID:
ELMD3_HUMAN

ALTERNATIVE NAMES:
RNA-binding motif and ELMO domain-containing protein 1; RNA-binding motif protein 29; RNA-binding protein 29

ALTERNATIVE UPACC:
Q96FG2; B8ZZD6; D6W5K4; Q2M1K3; Q2XSU3; Q2XSU4; Q8NAC1; Q8TCK4; Q8WV70; Q8WY75; Q9H6Q8

BACKGROUND:
The ELMO domain-containing protein 3, identified by its alternative names such as RNA-binding motif and ELMO domain-containing protein 1, RNA-binding motif protein 29, and RNA-binding protein 29, serves as a GTPase-activating protein for ARL2. This protein's activity is essential for maintaining cellular functions and highlights its significance in biological systems.

THERAPEUTIC SIGNIFICANCE:
Given its association with both autosomal recessive and dominant forms of deafness, ELMO domain-containing protein 3 represents a promising avenue for research into hearing loss treatments. The connection to deafness, autosomal recessive, 88, and deafness, autosomal dominant, 81, underscores the therapeutic potential of targeting this protein in future drug development efforts.

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