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.


Our selection of compounds is from a large virtual library of over 60 billion molecules. The production and distribution of these compounds are managed by Reaxense.


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.


Our top-notch dedicated system is used to design specialised libraries for enzymes.


 

Fig. 1. The screening 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.


PARTNER
Receptor.AI
 
UPACC
Q9H5I1

UPID:
SUV92_HUMAN

ALTERNATIVE NAMES:
Histone H3-K9 methyltransferase 2; Lysine N-methyltransferase 1B; Suppressor of variegation 3-9 homolog 2

ALTERNATIVE UPACC:
Q9H5I1; D3DRT4; Q5JSS4; Q5JSS5; Q6I9Y3; Q8ND06

BACKGROUND:
The enzyme Histone-lysine N-methyltransferase SUV39H2, with alternative names such as Lysine N-methyltransferase 1B, is integral to epigenetic mechanisms governing gene expression. By trimethylating 'Lys-9' of histone H3, it establishes a specific tag for epigenetic transcriptional repression, influencing heterochromatin formation and DNA methylation. Its interaction with RB1 and recruitment by the PER complex underline its significance in cell cycle control, transcriptional repression, and chromatin organization during spermatogenesis.

THERAPEUTIC SIGNIFICANCE:
Understanding the role of Histone-lysine N-methyltransferase SUV39H2 could open doors to potential therapeutic strategies.

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