AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Ubiquitin-like modifier-activating enzyme ATG7

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

This comprehensive focused library is produced on demand with state-of-the-art virtual screening and parameter assessment technology driven by Receptor.AI drug discovery platform. This approach outperforms traditional methods and provides higher-quality compounds with superior activity, selectivity and safety.

The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is facilitated by our partner Reaxense.

The library includes a list of the most effective modulators, each annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Furthermore, each compound is shown with its optimal docking poses, affinity scores, and activity scores, offering a detailed summary.

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

 Fig. 1. The sreening workflow of Receptor.AI

The procedure entails thorough molecular simulations of the catalytic and allosteric binding pockets, accompanied by ensemble virtual screening that factors in their conformational flexibility. When developing modulators, the structural modifications brought about by reaction intermediates are factored in to optimize 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

Reaxense

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:

Ubiquitin-like modifier-activating enzyme ATG7, also known as ATG12-activating enzyme E1 ATG7 and Autophagy-related protein 7, plays a pivotal role in autophagy, facilitating the conjugation of ATG12 with ATG5 and the ATG8 family proteins with phosphatidylethanolamine. This process is essential for the formation of autophagosomes, contributing to cellular homeostasis by regulating mitochondrial quantity and quality, modulating p53/TP53 activity, and supporting axonal homeostasis.

Therapeutic significance:

ATG7's involvement in Spinocerebellar ataxia, autosomal recessive, 31, underscores its potential as a therapeutic target. Understanding the role of ATG7 could open doors to potential therapeutic strategies for treating neurodegenerative diseases and conditions related to autophagy dysfunction.

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