Focused On-demand Libraries - Receptor.AI 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. Reaxense helps in synthesizing and delivering these compounds.


Contained in the library are leading modulators, each labelled with 38 ADME-Tox and 32 physicochemical and drug-likeness qualities. In addition, each compound is illustrated with its optimal docking poses, affinity scores, and activity scores, giving a complete picture.


We use our state-of-the-art dedicated workflow for designing focused libraries for enzymes.


 

Fig. 1. The screening 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.


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
Receptor.AI
 
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:
The protein Mannosyl-oligosaccharide 1,2-alpha-mannosidase IC, with alternative names HMIC and Mannosidase alpha class 1C member 1, is involved in the crucial process of trimming alpha-1,2-linked mannose residues during the maturation of Asn-linked oligosaccharides. This enzymatic activity is essential for the production of Man(8)GlcNAc(2), Man(6)GlcNAc, and Man(5)GlcNAc, which are key steps in ensuring the proper folding and functionality of glycoproteins.

THERAPEUTIC SIGNIFICANCE:
The exploration of Mannosyl-oligosaccharide 1,2-alpha-mannosidase IC's function offers a promising avenue for the development of novel therapeutic approaches. Given its pivotal role in the biosynthesis and maturation of glycoproteins, targeting this enzyme could lead to breakthroughs in treating conditions associated with aberrant glycosylation and protein folding.

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