AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Rho-related GTP-binding protein RhoH

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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.

We pick out particular compounds from an extensive virtual database of more than 60 billion molecules. The preparation and shipment of these compounds are facilitated by our associate 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 employ our advanced, specialised process to create targeted libraries.

 Fig. 1. The sreening 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.

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

Reaxense

upacc

Q15669

UPID:

RHOH_HUMAN

Alternative names:

GTP-binding protein TTF; Translocation three four protein

Alternative UPACC:

Q15669

Background:

Rho-related GTP-binding protein RhoH, also known as GTP-binding protein TTF and Translocation three four protein, plays a pivotal role in the regulation of hematopoietic progenitor cell proliferation, survival, and migration. It is a critical regulator of thymocyte development and T-cell antigen receptor signaling, facilitating the recruitment and activation of ZAP70, essential for thymocyte maturation and mast cell function.

Therapeutic significance:

Given its involvement in Epidermodysplasia verruciformis 4, a condition with a high risk of skin carcinoma, RhoH's functional pathways offer a promising target for therapeutic intervention. Understanding the role of RhoH could open doors to potential therapeutic strategies.

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