Focused On-demand Libraries - Receptor.AI 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.


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.


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.


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


 

Fig. 1. The screening workflow of Receptor.AI

By deploying molecular simulations, our approach comprehensively covers a broad array of proteins, tracking their flexibility and dynamics individually and within complexes. Ensemble virtual screening is utilised to take into account conformational dynamics, identifying pivotal binding sites located within functional regions and at allosteric locations. This thorough exploration ensures that every conceivable mechanism of action is considered, aiming to identify new therapeutic targets and advance lead compounds throughout a vast spectrum of biological functions.


Several key aspects differentiate our library:


  • Receptor.AI compiles an all-encompassing dataset on the target protein, including historical experiments, literature data, known ligands, and structural insights, maximising the chances of prioritising the most pertinent compounds.

  • The platform employs state-of-the-art molecular simulations to identify potential binding sites, ensuring the focused library is primed for discovering allosteric inhibitors and binders of concealed pockets.

  • Over 50 customisable AI models, thoroughly evaluated in various drug discovery endeavours and research projects, make Receptor.AI both efficient and accurate. This technology is integral to the development of our focused libraries.

  • In addition to generating focused libraries, Receptor.AI offers a full range of services and solutions for every step of preclinical drug discovery, with a pricing model based on success, thereby reducing risk and promoting joint project success.


PARTNER
Receptor.AI
 
UPACC
Q9H4E5

UPID:
RHOJ_HUMAN

ALTERNATIVE NAMES:
Ras-like protein family member 7B; Tc10-like GTP-binding protein

ALTERNATIVE UPACC:
Q9H4E5; Q96KC1

BACKGROUND:
The Rho-related GTP-binding protein RhoJ, known alternatively as Ras-like protein family member 7B and Tc10-like GTP-binding protein, is specifically involved in the process of angiogenesis. It is required for the migration of endothelial cells, a critical step in vascular development, through its interaction with GLUL. RhoJ significantly influences the formation of F-actin-rich structures, which are vital for the migration of endothelial cells.

THERAPEUTIC SIGNIFICANCE:
The exploration of Rho-related GTP-binding protein RhoJ's function offers promising avenues for therapeutic intervention. Given its crucial role in angiogenesis and the migration of endothelial cells, targeting RhoJ could lead to innovative treatments for diseases where angiogenesis plays a key role.

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