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.


From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Reaxense aids in their synthesis and provision.


The library features a range of promising modulators, each detailed with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Plus, each compound is presented with its ideal docking poses, affinity scores, and activity scores, ensuring a thorough insight.


We employ our advanced, specialised process to create targeted 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.


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
O60547

UPID:
GMDS_HUMAN

ALTERNATIVE NAMES:
GDP-D-mannose dehydratase

ALTERNATIVE UPACC:
O60547; E9PI88; O75357; Q5T954; Q6FH09; Q9UGZ3; Q9UJK9

BACKGROUND:
The enzyme GDP-mannose 4,6 dehydratase, alternatively known as GDP-D-mannose dehydratase, catalyzes a critical step in the metabolic pathway leading to the production of GDP-fucose. This metabolite is indispensable for the fucosylation of glycoproteins and glycolipids, which are vital for various biological processes including immune response and development.

THERAPEUTIC SIGNIFICANCE:
Exploring the function of GDP-mannose 4,6 dehydratase offers a promising avenue for drug discovery. As fucosylation plays a significant role in immune system modulation and cell signaling, targeting this enzyme could lead to innovative treatments for diseases characterized by abnormal cell signaling and immune responses.

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.