Focused On-demand Library for UDP-GalNAc:beta-1,3-N-acetylgalactosaminyltransferase 2

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

The specialised, focused library is developed on demand with the most recent virtual screening and parameter assessment technology, guided by the Receptor.AI drug discovery platform. This approach exceeds the capabilities of traditional methods and offers compounds with higher 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 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.

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

Fig. 1. The sreening workflow of Receptor.AI

This approach involves comprehensive molecular simulations of the catalytic and allosteric binding pockets and ensemble virtual screening that accounts for their conformational flexibility. In the case of designing modulators, the structural adjustments caused by reaction intermediates are considered to improve 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

Q8NCR0

UPID:

B3GL2_HUMAN

Alternative names:

Beta-1,3-N-acetylgalactosaminyltransferase II

Alternative UPACC:

Q8NCR0; Q59GR3; Q5TCI3; Q96AL7

Background:

UDP-GalNAc:beta-1,3-N-acetylgalactosaminyltransferase 2, also known as Beta-1,3-N-acetylgalactosaminyltransferase II, plays a crucial role in the synthesis of unique carbohydrate structures on glycoproteins. It is specifically involved in the glycosylation of alpha-dystroglycan, a process essential for the binding of extracellular proteins with high affinity.

Therapeutic significance:

The protein's malfunction is linked to Muscular dystrophy-dystroglycanopathy congenital with brain and eye anomalies A11, a severe disorder leading to early life fatality. Understanding its function could pave the way for novel therapeutic approaches targeting these debilitating conditions.