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.


Our selection of compounds is from a large virtual library of over 60 billion molecules. The production and distribution of these compounds are managed 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.


Our high-tech, dedicated method is applied to construct targeted libraries.


 

Fig. 1. The screening workflow of Receptor.AI

Our strategy employs molecular simulations to explore an extensive range of proteins, capturing their dynamics both individually and within complexes with other proteins. Through ensemble virtual screening, we address proteins' conformational mobility, uncovering key binding sites at both functional regions and remote allosteric locations. This comprehensive investigation ensures a thorough assessment of all potential mechanisms of action, with the goal of discovering innovative therapeutic targets and lead molecules across across diverse biological functions.


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
Q96C11

UPID:
FGGY_HUMAN

ALTERNATIVE NAMES:
D-ribulokinase FGGY

ALTERNATIVE UPACC:
Q96C11; B1AK92; B1AK93; B1AK94; B2RDR8; D3DQ56; Q9HA63; Q9NV20

BACKGROUND:
FGGY carbohydrate kinase domain-containing protein, alternatively named D-ribulokinase FGGY, is pivotal in metabolite repair mechanisms. It ensures the phosphorylation of D-ribulose to D-ribulose 5-phosphate, averting the harmful buildup of free D-ribulose. This activity is speculated to be crucial for the pentose phosphate pathway's regulation, highlighting its significance in cellular metabolism.

THERAPEUTIC SIGNIFICANCE:
The association of the FGGY carbohydrate kinase domain-containing protein with amyotrophic lateral sclerosis, marked by fatal paralysis and motor neuron degeneration, underscores the importance of research into this protein. Understanding its role could lead to groundbreaking therapeutic interventions.

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