Focused On-demand Library for Histone-lysine N-methyltransferase EHMT2

Focused On-demand Libraries - Receptor.AI 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. Reaxense aids in their synthesis and provision.


The library features a range of promising modulators, each detailed with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Plus, each compound is presented with its ideal docking poses, affinity scores, and activity scores, ensuring a thorough insight.


We use our state-of-the-art dedicated workflow for designing focused libraries for enzymes.


 

Fig. 1. The screening workflow of Receptor.AI

It includes comprehensive molecular simulations of the catalytic and allosteric binding pockets and the ensemble virtual screening accounting for their conformational mobility. In the case of designing modulators, the structural changes induced by reaction intermediates are taken into account to leverage activity and selectivity.


Several key aspects differentiate our library:


  • Receptor.AI compiles an all-encompassing dataset on the target protein, including historical experiments, literature data, known ligands, and structural insights, maximising the chances of prioritising the most pertinent compounds.

  • The platform employs state-of-the-art molecular simulations to identify potential binding sites, ensuring the focused library is primed for discovering allosteric inhibitors and binders of concealed pockets.

  • Over 50 customisable AI models, thoroughly evaluated in various drug discovery endeavours and research projects, make Receptor.AI both efficient and accurate. This technology is integral to the development of our focused libraries.

  • In addition to generating focused libraries, Receptor.AI offers a full range of services and solutions for every step of preclinical drug discovery, with a pricing model based on success, thereby reducing risk and promoting joint project success.

PARTNER
Receptor.AI
 
UPACC
Q96KQ7

UPID:
EHMT2_HUMAN

ALTERNATIVE NAMES:
Euchromatic histone-lysine N-methyltransferase 2; HLA-B-associated transcript 8; Histone H3-K9 methyltransferase 3; Lysine N-methyltransferase 1C; Protein G9a

ALTERNATIVE UPACC:
Q96KQ7; B0UZY2; Q14349; Q5JP83; Q5JQ92; Q5JQA1; Q5JQG3; Q6PK06; Q96MH5; Q96QD0; Q9UQL8; Q9Y331

BACKGROUND:
The protein G9a, known formally as Histone-lysine N-methyltransferase EHMT2, is essential for epigenetic transcriptional repression through methylation of 'Lys-9' on histone H3. Beyond its role in histone modification, EHMT2 contributes to DNA methylation and replication, indicating its broad impact on genomic stability and expression. The enzyme's activity extends to non-histone proteins, including the tumor suppressor p53, underscoring its significance in cellular function and disease mechanisms.

THERAPEUTIC SIGNIFICANCE:
The exploration of Histone-lysine N-methyltransferase EHMT2's functions offers promising avenues for therapeutic intervention. Given its central role in regulating gene expression and maintaining genomic integrity, targeting EHMT2 could lead to innovative treatments for diseases where epigenetic regulation and DNA repair mechanisms are disrupted.

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