AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Bifunctional arginine demethylase and lysyl-hydroxylase JMJD6

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.

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 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.

partner

Reaxense

upacc

Q6NYC1

UPID:

JMJD6_HUMAN

Alternative names:

Histone arginine demethylase JMJD6; JmjC domain-containing protein 6; Jumonji domain-containing protein 6; Lysyl-hydroxylase JMJD6; Peptide-lysine 5-dioxygenase JMJD6; Phosphatidylserine receptor

Alternative UPACC:

Q6NYC1; B3KMN8; B4DGX1; Q86VY0; Q8IUM5; Q9Y4E2

Background:

Bifunctional arginine demethylase and lysyl-hydroxylase JMJD6, also known as Histone arginine demethylase JMJD6, plays a pivotal role in RNA splicing, histone modification, and embryonic development. It exhibits unique enzymatic activities, including arginine demethylation and lysyl-hydroxylation, impacting gene expression and chromatin dynamics. Its ability to modify histones and other proteins underscores its significance in epigenetic regulation.

Therapeutic significance:

Understanding the role of Bifunctional arginine demethylase and lysyl-hydroxylase JMJD6 could open doors to potential therapeutic strategies. Its involvement in crucial biological processes such as RNA splicing and histone modification positions it as a key target for drug discovery efforts aimed at treating diseases with epigenetic underpinnings.

Looking for more information on this library or underlying technology? Fill out the form below and we'll be in touch with all the details you need.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.