Focused On-demand Library for Peroxiredoxin-1

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

Our detailed focused library is generated on demand with advanced virtual screening and parameter assessment technology powered by the Receptor.AI drug discovery platform. This method surpasses traditional approaches, delivering compounds of better quality with enhanced activity, selectivity, and safety.

From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Our collaborator, 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.

Our high-tech, dedicated method is applied to construct targeted libraries for enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

The method includes detailed molecular simulations of the catalytic and allosteric binding pockets, along with ensemble virtual screening that considers their conformational flexibility. In the design of modulators, structural changes induced by reaction intermediates are taken into account to enhance 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.







Alternative names:

Natural killer cell-enhancing factor A; Proliferation-associated gene protein; Thioredoxin peroxidase 2; Thioredoxin-dependent peroxide reductase 2; Thioredoxin-dependent peroxiredoxin 1

Alternative UPACC:

Q06830; B5BU26; D3DPZ8; P35703; Q2V576; Q5T154; Q5T155


Peroxiredoxin-1, known by alternative names such as Natural killer cell-enhancing factor A and Thioredoxin peroxidase 2, plays a pivotal role in cellular defense mechanisms. It catalyzes the reduction of hydrogen peroxide and organic hydroperoxides, safeguarding cells from oxidative stress. This protein is integral in detoxifying peroxides, serving as a sensor for hydrogen peroxide-mediated signaling events, and may influence the signaling cascades of growth factors and tumor necrosis factor-alpha.

Therapeutic significance:

Understanding the role of Peroxiredoxin-1 could open doors to potential therapeutic strategies. Its involvement in oxidative stress response and cellular signaling pathways highlights its potential as a target for therapeutic intervention in conditions where these processes are dysregulated.

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