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

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.


We carefully select specific compounds from a vast collection of over 60 billion molecules in virtual chemical space. Reaxense helps in synthesizing and delivering these compounds.


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.


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


 

Fig. 1. The screening workflow of Receptor.AI

Our methodology leverages molecular simulations to examine a vast array of proteins, capturing their dynamics in both isolated forms and in complexes with other proteins. Through ensemble virtual screening, we thoroughly account for the protein's conformational mobility, identifying critical binding sites within functional regions and distant allosteric locations. This detailed exploration ensures that we comprehensively assess every possible mechanism of action, with the objective of identifying novel therapeutic targets and lead compounds that span a wide spectrum of biological functions.


Our library distinguishes itself through several key aspects:


  • The Receptor.AI platform integrates all available data about the target protein, including past experiments, literature data, known ligands, structural information and more. This consolidated approach maximises the probability of prioritising highly relevant compounds.

  • The platform uses sophisticated molecular simulations to identify possible binding sites so that the compounds in the focused library are suitable for discovering allosteric inhibitors and the binders for cryptic pockets.

  • The platform integrates over 50 highly customisable AI models, which are thoroughly tested and validated on a multitude of commercial drug discovery programs and research projects. It is designed to be efficient, reliable and accurate. All this power is utilised when producing the focused libraries.

  • In addition to producing the focused libraries, Receptor.AI provides services and end-to-end solutions at every stage of preclinical drug discovery. The pricing model is success-based, which reduces your risks and leverages the mutual benefits of the project's success.

PARTNER
Receptor.AI
 
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:
The ER lumen protein-retaining receptor 2, known alternatively as ERD2-like protein 1 or KDEL receptor 2, is pivotal in maintaining the localization of endoplasmic reticulum proteins. It achieves this by binding to the K-D-E-L sequence motif, facilitating their recycling from the Golgi, in a process that is most efficient at slightly acidic pH levels.

THERAPEUTIC SIGNIFICANCE:
Linked to the development of Osteogenesis imperfecta 21, a disease causing bone fragility and susceptibility to fractures, the ER lumen protein-retaining receptor 2's function is of significant interest. Exploring its role further could lead to innovative treatments for this and potentially other skeletal disorders.

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