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.


The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is facilitated by Reaxense.


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


 

Fig. 1. The screening workflow of Receptor.AI

It includes in-depth molecular simulations of both the catalytic and allosteric binding pockets, with ensemble virtual screening focusing on their conformational flexibility. For modulators, the process includes considering the structural shifts due to reaction intermediates to boost activity and selectivity.


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
P32119

UPID:
PRDX2_HUMAN

ALTERNATIVE NAMES:
Natural killer cell-enhancing factor B; PRP; Thiol-specific antioxidant protein; Thioredoxin peroxidase 1; Thioredoxin-dependent peroxide reductase 1; Thioredoxin-dependent peroxiredoxin 2

ALTERNATIVE UPACC:
P32119; A8K0C0; P31945; P32118; P35701; Q6FHG4; Q92763; Q9UC23

BACKGROUND:
Peroxiredoxin-2, with alternative names such as Natural killer cell-enhancing factor B and Thioredoxin-dependent peroxide reductase 1, is a thiol-specific peroxidase. It is pivotal in reducing harmful peroxides within the cell, contributing to cellular protection against oxidative damage. The protein's involvement in hydrogen peroxide-mediated signaling events underscores its significance in cellular growth and immune response mechanisms.

THERAPEUTIC SIGNIFICANCE:
The exploration of Peroxiredoxin-2's functions offers promising avenues for drug discovery. By modulating the intracellular concentrations of H(2)O(2), it plays a vital role in cellular signaling pathways, suggesting its potential in developing treatments for conditions arising from oxidative stress and abnormal cell signaling.

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