Focused On-demand Library for Major histocompatibility complex class I-related gene protein

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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.

The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is facilitated by our partner Reaxense.

The library includes a list of the most promising modulators annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Also, each compound is presented with its optimal docking poses, affinity scores, and activity scores, providing a comprehensive overview.

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.

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.







Alternative names:

Class I histocompatibility antigen-like protein

Alternative UPACC:

Q95460; A8K2V9; B4E3B1; O97985; O97986; Q53GM1; Q95HB8; Q9MY23; Q9NPL2; Q9TQB3; Q9TQB9; Q9TQK3


The Major histocompatibility complex class I-related gene protein, also known as Class I histocompatibility antigen-like protein, plays a pivotal role in immune surveillance. It specializes in presenting microbial pyrimidine-based metabolites to T cell receptors on mucosal-associated invariant T cells, guiding the immune response against microbial metabolites at mucosal barriers. This protein, in complex with B2M, preferentially presents riboflavin-derived metabolites, crucial for recognizing diverse microbes and pathogens.

Therapeutic significance:

Understanding the role of Major histocompatibility complex class I-related gene protein could open doors to potential therapeutic strategies. Its ability to act as an immune sensor of cancer cell metabolome and present tumor-specific metabolites to T cells highlights its therapeutic potential in cancer immunotherapy.

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