AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Autophagy-related protein 9A

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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 carefully select specific compounds from a vast collection of over 60 billion molecules in virtual chemical space. Our partner Reaxense helps in synthesizing and delivering these compounds.

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 sreening workflow of Receptor.AI

Utilising molecular simulations, our approach thoroughly examines a wide array of proteins, tracking their conformational changes individually and within complexes. Ensemble virtual screening enables us to address conformational flexibility, revealing essential binding sites at functional regions and allosteric locations. Our rigorous analysis guarantees that no potential mechanism of action is overlooked, aiming to uncover new therapeutic targets and lead compounds 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

Reaxense

upacc

Q7Z3C6

UPID:

ATG9A_HUMAN

Alternative names:

APG9-like 1; mATG9

Alternative UPACC:

Q7Z3C6; Q3ZAQ6; Q6P0N7; Q7Z317; Q7Z320; Q8NDK6; Q8WU65; Q9BVL5; Q9H6L1; Q9HAG7

Background:

Autophagy-related protein 9A (APG9-like 1, mATG9) is a pivotal phospholipid scramblase facilitating autophagy through autophagosomal membrane expansion. It dynamically transitions between the preautophagosomal structure and the cytoplasmic vesicle pool, enriching the autophagosome with necessary phospholipids. Its lipid scramblase activity is crucial for distributing phospholipids across the bilayer, driven by ATG2-mediated transfer, thereby promoting membrane growth. Additionally, it aids in recruiting PI4KB to the autophagosome initiation site via ARFIP2, enhancing phosphatidylinositol 4-phosphate availability.

Therapeutic significance:

Understanding the role of Autophagy-related protein 9A could open doors to potential therapeutic strategies.

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