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 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

Our strategy employs molecular simulations to explore an extensive range of proteins, capturing their dynamics both individually and within complexes with other proteins. Through ensemble virtual screening, we address proteins' conformational mobility, uncovering key binding sites at both functional regions and remote allosteric locations. This comprehensive investigation ensures a thorough assessment of all potential mechanisms of action, with the goal of discovering innovative therapeutic targets and lead molecules across across diverse biological functions.


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
Q7Z7H8

UPID:
RM10_HUMAN

ALTERNATIVE NAMES:
39S ribosomal protein L10, mitochondrial; 39S ribosomal protein L8, mitochondrial

ALTERNATIVE UPACC:
Q7Z7H8; A6NGJ4; Q96B80; Q96Q55

BACKGROUND:
Large ribosomal subunit protein uL10m, alternatively known as 39S ribosomal protein L10 and L8, is integral to mitochondrial protein synthesis. This protein's involvement in the translation process of the mitochondria underscores its importance in maintaining mitochondrial health and overall cellular energy homeostasis.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of Large ribosomal subunit protein uL10m offers a promising pathway to novel therapeutic approaches. Given its central role in mitochondrial function, targeting this protein could lead to breakthroughs in treating conditions associated with mitochondrial impairments.

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