AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Carbohydrate sulfotransferase 9

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

This extensive focused library is tailor-made using the latest virtual screening and parameter assessment technology, operated by the Receptor.AI drug discovery platform. This technique is more effective than traditional methods, offering compounds with improved activity, selectivity, and safety.

The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is facilitated by our partner Reaxense.

The library includes a list of the most effective modulators, each annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Furthermore, each compound is shown with its optimal docking poses, affinity scores, and activity scores, offering a detailed summary.

Our high-tech, dedicated method is applied to construct targeted 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.

partner

Reaxense

upacc

Q7L1S5

UPID:

CHST9_HUMAN

Alternative names:

GalNAc-4-O-sulfotransferase 2; N-acetylgalactosamine-4-O-sulfotransferase 2

Alternative UPACC:

Q7L1S5; Q6UX69; Q9BXH3; Q9BXH4; Q9BZW9

Background:

Carbohydrate sulfotransferase 9, also known as GalNAc-4-O-sulfotransferase 2, plays a crucial role in the biosynthesis of glycoprotein hormones such as lutropin and thyrotropin. It catalyzes the transfer of sulfate to the 4th position of GalNAc residues in N-glycans and O-glycans, essential for the proper functioning of these hormones. Its activity is notably higher towards carbonic anhydrase VI than lutropin, showcasing its specificity and importance in biochemical pathways.

Therapeutic significance:

Understanding the role of Carbohydrate sulfotransferase 9 could open doors to potential therapeutic strategies. Its involvement in the sulfation process essential for hormone activity highlights its potential as a target for modulating hormone-related disorders.

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