AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Peroxiredoxin-4

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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.

We carefully select specific compounds from a vast collection of over 60 billion molecules in virtual chemical space. Our partner Reaxense helps in synthesizing and delivering these compounds.

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 utilise our cutting-edge, exclusive workflow to develop focused libraries for enzymes.

 Fig. 1. The sreening 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 is unique due to several crucial aspects:

  • Receptor.AI compiles all relevant data on the target protein, such as past experimental results, literature findings, known ligands, and structural data, thereby enhancing the likelihood of focusing on the most significant compounds.
  • By utilizing advanced molecular simulations, the platform is adept at locating potential binding sites, rendering the compounds in the focused library well-suited for unearthing allosteric inhibitors and binders for hidden pockets.
  • The platform is supported by more than 50 highly specialized AI models, all of which have been rigorously tested and validated in diverse drug discovery and research programs. Its design emphasizes efficiency, reliability, and accuracy, crucial for producing focused libraries.
  • Receptor.AI extends beyond just creating focused libraries; it offers a complete spectrum of services and solutions during the preclinical drug discovery phase, with a success-dependent pricing strategy that reduces risk and fosters shared success in the project.

partner

Reaxense

upacc

Q13162

UPID:

PRDX4_HUMAN

Alternative names:

Antioxidant enzyme AOE372; Peroxiredoxin IV; Thioredoxin peroxidase AO372; Thioredoxin-dependent peroxide reductase A0372; Thioredoxin-dependent peroxiredoxin 4

Alternative UPACC:

Q13162; Q6FHT3

Background:

Peroxiredoxin-4, also known as Antioxidant enzyme AOE372, plays a crucial role in cellular defense mechanisms. It catalyzes the reduction of hydrogen peroxide and organic hydroperoxides, thereby protecting cells from oxidative stress. This enzyme is also involved in hydrogen peroxide-mediated signaling, influencing NF-kappa-B activation through the modulation of I-kappa-B-alpha phosphorylation.

Therapeutic significance:

Understanding the role of Peroxiredoxin-4 could open doors to potential therapeutic strategies. Its involvement in oxidative stress response and signaling pathways highlights its potential as a target for treating diseases where oxidative damage is a key factor.

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