AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Epithelial cell adhesion molecule

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

This extensive focused library is tailor-made using the latest virtual screening and parameter assessment technology, operated by the Receptor.AI drug discovery platform. This technique is more effective than traditional methods, offering compounds with improved 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 our partner Reaxense.

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.

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

P16422

UPID:

EPCAM_HUMAN

Alternative names:

Adenocarcinoma-associated antigen; Cell surface glycoprotein Trop-1; Epithelial cell surface antigen; Epithelial glycoprotein; Epithelial glycoprotein 314; KS 1/4 antigen; KSA; Major gastrointestinal tumor-associated protein GA733-2; Tumor-associated calcium signal transducer 1

Alternative UPACC:

P16422; P18180; Q6FG26; Q6FG49; Q96C47; Q9UCD0

Background:

The Epithelial Cell Adhesion Molecule (EpCAM), also known as Tumor-associated calcium signal transducer 1, plays a crucial role in cell-cell adhesion, providing an immunological barrier against mucosal infection. It is involved in the proliferation and differentiation of embryonic stem cells and up-regulates the expression of key proteins such as FABP5, MYC, and cyclins A and E.

Therapeutic significance:

EpCAM's association with Diarrhea 5, characterized by intestinal epithelial cell dysplasia, and Lynch syndrome 8, linked to increased cancer susceptibility, underscores its potential as a target for therapeutic intervention in both congenital intestinal disorders and hereditary cancers.

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