Focused On-demand Library for Glutathione S-transferase Mu 2

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.

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 procedure entails thorough molecular simulations of the catalytic and allosteric binding pockets, accompanied by ensemble virtual screening that factors in their conformational flexibility. When developing modulators, the structural modifications brought about by reaction intermediates are factored in to optimize 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 class-mu 2; GSTM2-2

Alternative UPACC:

P28161; B4DRY4; E9PEM9; Q2M318; Q5TZY5; Q8WWE1


Glutathione S-transferase Mu 2 (GSTM2-2), also known as GST class-mu 2, plays a crucial role in detoxifying cells by conjugating reduced glutathione to a wide array of hydrophobic electrophiles. This enzyme is pivotal in metabolizing endogenous and exogenous compounds, including carcinogens, therapeutic drugs, and products of oxidative stress, thereby protecting cells from toxic insults. Additionally, GSTM2-2 is involved in the formation of novel hepoxilin regioisomers, highlighting its significance in lipid metabolism.

Therapeutic significance:

Understanding the role of Glutathione S-transferase Mu 2 could open doors to potential therapeutic strategies. Its involvement in detoxification and lipid metabolism suggests that modulating its activity could be beneficial in treating conditions related to oxidative stress and metabolic disorders.

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