AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Junctional adhesion molecule A

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.

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 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 high-tech, dedicated method is applied to construct targeted 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.

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

Reaxense

upacc

Q9Y624

UPID:

JAM1_HUMAN

Alternative names:

Junctional adhesion molecule 1; Platelet F11 receptor; Platelet adhesion molecule 1

Alternative UPACC:

Q9Y624; B7Z941

Background:

Junctional adhesion molecule A (JAM-A), also known as Platelet F11 receptor and Platelet adhesion molecule 1, plays a pivotal role in epithelial tight junction formation. It is crucial in the early stages of cell junctions, recruiting PARD3 to prevent its interaction with JAM1, thus inhibiting tight junction assembly. JAM-A is also key in monocyte transmigration, maintaining epithelial barrier integrity, and acts as a ligand for integrin alpha-L/beta-2, facilitating memory T-cell and neutrophil transmigration. Additionally, it is involved in platelet activation and serves as a receptor for pathogens like Mammalian reovirus sigma-1 and Human Rotavirus strain Wa.

Therapeutic significance:

Understanding the role of Junctional adhesion molecule A could open doors to potential therapeutic strategies, particularly in enhancing epithelial barrier function and modulating immune cell transmigration.

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