Focused On-demand Library for Glutathione S-transferase kappa 1

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.

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.

We utilise our cutting-edge, exclusive workflow to develop focused 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 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.







Alternative names:

GST 13-13; GST class-kappa; GSTK1-1; Glutathione S-transferase subunit 13

Alternative UPACC:

Q9Y2Q3; B4DIH1; B4DSY2; Q6P4H0; Q7Z520; Q9P1S4


Glutathione S-transferase kappa 1 (GSTK1-1), also known as GST 13-13 and GST class-kappa, plays a crucial role in cellular detoxification. It achieves this by catalyzing the conjugation of glutathione to a wide range of exogenous and endogenous compounds. This enzyme exhibits significant activity with the model substrate 1-chloro-2,4-dinitrobenzene (CDNB), highlighting its importance in neutralizing harmful substances.

Therapeutic significance:

Understanding the role of Glutathione S-transferase kappa 1 could open doors to potential therapeutic strategies. Its pivotal function in detoxification processes positions it as a key target for developing treatments aimed at enhancing the body's ability to neutralize toxic compounds.

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