Focused On-demand Library for HLA class I histocompatibility antigen, alpha chain G

Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

The specialised, focused library is developed on demand with the most recent virtual screening and parameter assessment technology, guided by the Receptor.AI drug discovery platform. This approach exceeds the capabilities of traditional methods and offers compounds with higher 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

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 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
Receptor.AI
 
UPACC
P17693

UPID:
HLAG_HUMAN

ALTERNATIVE NAMES:
HLA G antigen; MHC class I antigen G

ALTERNATIVE UPACC:
P17693

BACKGROUND:
The HLA-G antigen, recognized for its critical function in immune regulatory processes at the maternal-fetal interface, binds a limited repertoire of self-peptides. This peptide-bound HLA-G-B2M complex serves as a crucial ligand for inhibitory and activating receptors, modulating the activity of uterine immune cells. Its interactions are essential for promoting fetal development, vascular remodeling in early pregnancy, and maintaining a balance between tolerance and antiviral immunity at the maternal-fetal interface.

THERAPEUTIC SIGNIFICANCE:
Exploring the multifaceted role of HLA-G offers a promising avenue for developing novel therapeutic approaches aimed at fostering maternal-fetal tolerance and potentially mitigating immune-mediated complications.

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