Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

The specialised, focused library is developed on demand with the most recent virtual screening and parameter assessment technology, guided by the Receptor.AI drug discovery platform. This approach exceeds the capabilities of traditional methods and offers compounds with higher 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.


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
O95352

UPID:
ATG7_HUMAN

ALTERNATIVE NAMES:
ATG12-activating enzyme E1 ATG7; Autophagy-related protein 7; Ubiquitin-activating enzyme E1-like protein

ALTERNATIVE UPACC:
O95352; B4E170; E9PB95; Q7L8L0; Q9BWP2; Q9UFH4

BACKGROUND:
The Ubiquitin-like modifier-activating enzyme ATG7, known for its roles in autophagy and cellular homeostasis, activates crucial proteins for autophagosome formation. It supports mitochondrial regulation, p53/TP53 activity during metabolic stress, axonal homeostasis, and the autophagic degradation of CRY1, influencing liver clock and glucose metabolism.

THERAPEUTIC SIGNIFICANCE:
Given ATG7's critical function in Spinocerebellar ataxia, autosomal recessive, 31, by affecting gene variants, it emerges as a key target for drug discovery. Exploring ATG7's mechanisms offers a promising pathway for developing novel treatments for autophagy-related disorders.

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