Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

Our detailed focused library is generated on demand with advanced virtual screening and parameter assessment technology powered by the Receptor.AI drug discovery platform. This method surpasses traditional approaches, delivering compounds of better quality with enhanced activity, selectivity, and safety.


We pick out particular compounds from an extensive virtual database of more than 60 billion molecules. The preparation and shipment of these compounds are facilitated by Reaxense.


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.


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


 

Fig. 1. The screening 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.


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
Q9NY97

UPID:
B3GN2_HUMAN

ALTERNATIVE NAMES:
Beta-1,3-N-acetylglucosaminyltransferase 1; Beta-1,3-galactosyltransferase 7; Beta-3-Gx-T7; UDP-Gal:beta-GlcNAc beta-1,3-galactosyltransferase 7; UDP-GlcNAc:betaGal beta-1,3-N-acetylglucosaminyltransferase 2; UDP-galactose:beta-N-acetylglucosamine beta-1,3-galactosyltransferase 7

ALTERNATIVE UPACC:
Q9NY97; Q54AC1; Q9NQQ9; Q9NQR0; Q9NUT9

BACKGROUND:
The enzyme N-acetyllactosaminide beta-1,3-N-acetylglucosaminyltransferase 2, recognized by multiple names including Beta-3-Gx-T7 and UDP-Gal:beta-GlcNAc beta-1,3-galactosyltransferase 7, is integral to the synthesis of poly-N-acetyllactosamine. It catalyzes both the initiation and elongation processes of poly-N-acetyllactosamine chains, with a strong preference for specific Gal(beta1-4)Glc(NAc)-based acceptors.

THERAPEUTIC SIGNIFICANCE:
Understanding the role of N-acetyllactosaminide beta-1,3-N-acetylglucosaminyltransferase 2 could open doors to potential therapeutic strategies.

Looking for more information on this library or underlying technology? Fill out the form below and we will be in touch with all the details you need.