AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Endoplasmic reticulum-Golgi intermediate compartment protein 1

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.

From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Our collaborator, Reaxense, aids in their synthesis and provision.

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.

We use our state-of-the-art dedicated workflow for designing focused libraries.

 Fig. 1. The sreening 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 is unique due to several crucial aspects:

  • Receptor.AI compiles all relevant data on the target protein, such as past experimental results, literature findings, known ligands, and structural data, thereby enhancing the likelihood of focusing on the most significant compounds.
  • By utilizing advanced molecular simulations, the platform is adept at locating potential binding sites, rendering the compounds in the focused library well-suited for unearthing allosteric inhibitors and binders for hidden pockets.
  • The platform is supported by more than 50 highly specialized AI models, all of which have been rigorously tested and validated in diverse drug discovery and research programs. Its design emphasizes efficiency, reliability, and accuracy, crucial for producing focused libraries.
  • Receptor.AI extends beyond just creating focused libraries; it offers a complete spectrum of services and solutions during the preclinical drug discovery phase, with a success-dependent pricing strategy that reduces risk and fosters shared success in the project.

partner

Reaxense

upacc

Q969X5

UPID:

ERGI1_HUMAN

Alternative names:

ER-Golgi intermediate compartment 32 kDa protein

Alternative UPACC:

Q969X5; Q9H0L0; Q9H2J2; Q9ULN9

Background:

The Endoplasmic reticulum-Golgi intermediate compartment protein 1, also known as ER-Golgi intermediate compartment 32 kDa protein, plays a crucial role in cellular transport mechanisms, specifically facilitating transport between the endoplasmic reticulum and Golgi apparatus. This protein's function is vital for maintaining cellular homeostasis and ensuring the proper processing and trafficking of proteins.

Therapeutic significance:

Linked to Arthrogryposis multiplex congenita 2, neurogenic type (AMC2), a condition characterized by multiple joint contractures and muscle wasting, this protein's study offers insights into neurogenic defects. Understanding the role of Endoplasmic reticulum-Golgi intermediate compartment protein 1 could open doors to potential therapeutic strategies for AMC2, highlighting its significance in neurogenic disease research.

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