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.


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

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 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
Receptor.AI
 
UPACC
O43529

UPID:
CHSTA_HUMAN

ALTERNATIVE NAMES:
HNK-1 sulfotransferase

ALTERNATIVE UPACC:
O43529; Q53T18

BACKGROUND:
Carbohydrate sulfotransferase 10, known for its alternative name HNK-1 sulfotransferase, is integral in the creation of sulfated glucuronyl-lactosaminyl residues. These residues are key components of many neural recognition molecules, aiding in ontogenetic development and adult synaptic plasticity. The enzyme's activity includes sulfating the terminal glucuronyl residue of laminin globular domain binding epitope on alpha-dystroglycan, which is essential for binding specificity to extracellular matrix components.

THERAPEUTIC SIGNIFICANCE:
Exploring the functionalities of Carbohydrate sulfotransferase 10 offers a promising avenue for developing novel therapeutic approaches, especially in the fields of neural development and hormone regulation.

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