AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for ER lumen protein-retaining receptor 2

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.

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.

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.

Our top-notch dedicated system is used to design specialised libraries.

 Fig. 1. The sreening 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

Reaxense

upacc

P33947

UPID:

ERD22_HUMAN

Alternative names:

ERD2-like protein 1; KDEL endoplasmic reticulum protein retention receptor 2

Alternative UPACC:

P33947; A4D2P4; Q6IPC5; Q96E30

Background:

ER lumen protein-retaining receptor 2, also known as ERD2-like protein 1 or KDEL endoplasmic reticulum protein retention receptor 2, plays a crucial role in cellular function. It binds the K-D-E-L sequence motif in endoplasmic reticulum resident proteins, ensuring their retention or recycling back from the Golgi apparatus. This process is pH-dependent, with optimal activity at pH 5-5.4.

Therapeutic significance:

Osteogenesis imperfecta 21, a connective tissue disorder characterized by bone fragility, is linked to mutations affecting this protein. Understanding the role of ER lumen protein-retaining receptor 2 could open doors to potential therapeutic strategies for this and related conditions.

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