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.


Our selection of compounds is from a large virtual library of over 60 billion molecules. The production and distribution of these compounds are managed by Reaxense.


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.


Our high-tech, dedicated method is applied to construct 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.


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
Q9NRX2

UPID:
RM17_HUMAN

ALTERNATIVE NAMES:
39S ribosomal protein L17, mitochondrial; LYST-interacting protein 2

ALTERNATIVE UPACC:
Q9NRX2; D3DQU3; Q6IAH8; Q96Q53; Q9C066

BACKGROUND:
Large ribosomal subunit protein bL17m, identified by its alternative names 39S ribosomal protein L17, mitochondrial and LYST-interacting protein 2, is integral to mitochondrial ribosomal operations. This protein's role in the synthesis of proteins within mitochondria highlights its significance in maintaining cellular vitality and energy production.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of Large ribosomal subunit protein bL17m holds promise for unveiling novel therapeutic approaches. Given its critical role in mitochondrial function, targeting this protein could lead to breakthroughs in treating mitochondrial diseases.

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