Focused On-demand Library for Glutaredoxin-3

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.

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.

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.

We employ our advanced, specialised process to create targeted libraries.

 Fig. 1. The sreening workflow of Receptor.AI

Our methodology leverages molecular simulations to examine a vast array of proteins, capturing their dynamics in both isolated forms and in complexes with other proteins. Through ensemble virtual screening, we thoroughly account for the protein's conformational mobility, identifying critical binding sites within functional regions and distant allosteric locations. This detailed exploration ensures that we comprehensively assess every possible mechanism of action, with the objective of identifying novel therapeutic targets and lead compounds that span a wide spectrum of 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.







Alternative names:

PKC-interacting cousin of thioredoxin; PKC-theta-interacting protein; Thioredoxin-like protein 2

Alternative UPACC:

O76003; B3KMP7; B3KMQ5; D3DRG2; Q5JV01; Q96CE0; Q9P1B0; Q9P1B1


Glutaredoxin-3, also known as PKC-interacting cousin of thioredoxin, PKC-theta-interacting protein, and Thioredoxin-like protein 2, plays a pivotal role in cytosolic iron-sulfur (Fe-S) cluster assembly. This process is crucial for the insertion of [2Fe-2S] clusters into specific cytosolic proteins, impacting various cellular functions. Additionally, Glutaredoxin-3 is a key regulator of cardiac hypertrophy and has a significant role in hemoglobin maturation, although it lacks thyoredoxin activity.

Therapeutic significance:

Understanding the role of Glutaredoxin-3 could open doors to potential therapeutic strategies, particularly in the context of cardiac health and blood disorders.

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