AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for N-acetylgalactosaminyltransferase 7

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.

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 utilise our cutting-edge, exclusive workflow to develop focused libraries for enzymes.

 Fig. 1. The sreening 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.

Our library is unique due to several crucial aspects:

  • Receptor.AI compiles all relevant data on the target protein, such as past experimental results, literature findings, known ligands, and structural data, thereby enhancing the likelihood of focusing on the most significant compounds.
  • By utilizing advanced molecular simulations, the platform is adept at locating potential binding sites, rendering the compounds in the focused library well-suited for unearthing allosteric inhibitors and binders for hidden pockets.
  • The platform is supported by more than 50 highly specialized AI models, all of which have been rigorously tested and validated in diverse drug discovery and research programs. Its design emphasizes efficiency, reliability, and accuracy, crucial for producing focused libraries.
  • Receptor.AI extends beyond just creating focused libraries; it offers a complete spectrum of services and solutions during the preclinical drug discovery phase, with a success-dependent pricing strategy that reduces risk and fosters shared success in the project.

partner

Reaxense

upacc

Q86SF2

UPID:

GALT7_HUMAN

Alternative names:

Polypeptide GalNAc transferase 7; Protein-UDP acetylgalactosaminyltransferase 7; UDP-GalNAc:polypeptide N-acetylgalactosaminyltransferase 7

Alternative UPACC:

Q86SF2; B3KQU3; Q7Z5W7; Q9UJ28

Background:

N-acetylgalactosaminyltransferase 7, known alternatively as Polypeptide GalNAc transferase 7, Protein-UDP acetylgalactosaminyltransferase 7, or UDP-GalNAc:polypeptide N-acetylgalactosaminyltransferase 7, plays a crucial role in O-linked oligosaccharide biosynthesis. This enzyme uniquely catalyzes the addition of N-acetyl-D-galactosamine residues to glycosylated peptides, requiring a pre-existing GalNAc on a peptide for further GalNAc additions, unlike its family counterparts.

Therapeutic significance:

Understanding the role of N-acetylgalactosaminyltransferase 7 could open doors to potential therapeutic strategies.

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