AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Mannosyl-oligosaccharide 1,2-alpha-mannosidase IC

Available from Reaxense
Predicted by Alphafold

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.

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.

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 method includes detailed molecular simulations of the catalytic and allosteric binding pockets, along with ensemble virtual screening that considers their conformational flexibility. In the design of modulators, structural changes induced by reaction intermediates are taken into account to enhance activity and selectivity.

Key features that set our library apart include:

  • The Receptor.AI platform integrates extensive information about the target protein, such as historical experiments, academic research, known ligands, and structural insights, thereby increasing the likelihood of identifying highly relevant compounds.
  • The platform’s sophisticated molecular simulations are designed to discover potential binding sites, ensuring that our focused library is optimal for the discovery of allosteric inhibitors and binders for cryptic pockets.
  • With over 50 customisable AI models, verified through extensive testing in commercial drug discovery and research, Receptor.AI is efficient, reliable, and precise. These models are essential in the production of our focused libraries.
  • Receptor.AI not only produces focused libraries but also provides full services and solutions at every stage of preclinical drug discovery, with a success-based pricing structure that aligns our interests with the success of your project.

partner

Reaxense

upacc

Q9NR34

UPID:

MA1C1_HUMAN

Alternative names:

HMIC; Mannosidase alpha class 1C member 1; Processing alpha-1,2-mannosidase IC

Alternative UPACC:

Q9NR34; A6NNE2; B2RNP2; Q9Y545

Background:

Mannosyl-oligosaccharide 1,2-alpha-mannosidase IC, also known as HMIC and Mannosidase alpha class 1C member 1, plays a crucial role in the maturation of Asn-linked oligosaccharides. It specifically trims alpha-1,2-linked mannose residues from Man(9)GlcNAc(2) to produce Man(8)GlcNAc(2), then Man(6)GlcNAc, and a small amount of Man(5)GlcNAc. This enzymatic process is vital for proper protein folding and quality control in the endoplasmic reticulum.

Therapeutic significance:

Understanding the role of Mannosyl-oligosaccharide 1,2-alpha-mannosidase IC could open doors to potential therapeutic strategies. Its critical function in protein maturation and quality control suggests that modulating its activity could have implications for diseases related to protein misfolding.

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