Focused On-demand Libraries - Receptor.AI 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.


Our selection of compounds is from a large virtual library of over 60 billion molecules. The production and distribution of these compounds are managed by Reaxense.


The library includes a list of the most promising modulators annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Also, each compound is presented with its optimal docking poses, affinity scores, and activity scores, providing a comprehensive overview.


We employ our advanced, specialised process to create targeted libraries.


 

Fig. 1. The screening workflow of Receptor.AI

Our methodology employs molecular simulations to explore a wide array of proteins, capturing their dynamic states both individually and within complexes. Through ensemble virtual screening, we address conformational mobility, uncovering binding sites within functional regions and remote allosteric locations. This thorough exploration ensures no potential mechanism of action is overlooked, aiming to discover novel therapeutic targets and lead compounds across an extensive 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
Q8N4V1

UPID:
EMC5_HUMAN

ALTERNATIVE NAMES:
Membrane magnesium transporter 1; Transmembrane protein 32

ALTERNATIVE UPACC:
Q8N4V1; B2R625; B4DIY3; D3DWG7; Q5JPP7

BACKGROUND:
The protein known as ER membrane protein complex subunit 5, with alternative names Membrane magnesium transporter 1 and Transmembrane protein 32, is a key component of the EMC, enabling the insertion of membrane proteins into the endoplasmic reticulum. It specializes in accommodating proteins with less hydrophobic transmembrane domains and those with charged or aromatic residues. Its involvement in the cotranslational insertion of multi-pass membrane proteins and the post-translational insertion of tail-anchored proteins is critical for maintaining the proper function and topology of various cellular proteins, including G protein-coupled receptors.

THERAPEUTIC SIGNIFICANCE:
Understanding the role of ER membrane protein complex subunit 5 could open doors to potential therapeutic strategies.

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