Focused On-demand Library for Histone-lysine N-methyltransferase SETD7

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.

In the library, a selection of top modulators is provided, each marked with 38 ADME-Tox and 32 parameters related to physicochemical properties and drug-likeness. Also, every compound comes with its best 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

It includes in-depth molecular simulations of both the catalytic and allosteric binding pockets, with ensemble virtual screening focusing on their conformational flexibility. For modulators, the process includes considering the structural shifts due to reaction intermediates to boost 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:

Histone H3-K4 methyltransferase SETD7; Lysine N-methyltransferase 7; SET domain-containing protein 7; SET7/9

Alternative UPACC:

Q8WTS6; B5WWL3; Q0VAH3; Q4W5A9; Q9C0E6


Histone-lysine N-methyltransferase SETD7, also known as SET7/9, plays a pivotal role in epigenetic transcriptional activation through specific methylation of 'Lys-4' on histone H3. This modification serves as a key marker for transcriptional activation, influencing the expression of critical genes, including those involved in collagenase and insulin production. Beyond histones, SETD7 exhibits methyltransferase activity towards non-histone proteins such as CGAS, p53/TP53, and TAF10, modulating their function and interaction with other cellular components.

Therapeutic significance:

Understanding the role of Histone-lysine N-methyltransferase SETD7 could open doors to potential therapeutic strategies.

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