Focused On-demand Library for MHC class I polypeptide-related sequence B

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 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.

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 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.

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.







Alternative names:


Alternative UPACC:

Q29980; A2AC57; A6NP85; B0UZ10; B2RAK2; O14499; O14500; O19798; O19799; O19800; O19801; O19802; O19803; O78099; O78100; O78101; O78102; O78103; O78104; P79525; P79541; Q5GR31; Q5GR37; Q5GR41; Q5GR42; Q5GR43; Q5GR44; Q5GR46; Q5GR48; Q5RIY6; Q5SSK1; Q5ST25; Q7JK51; Q7YQ89; Q861E6; Q9MY18; Q9MY19; Q9MY20; Q9UBH4; Q9UBZ8; Q9UEJ0; X6R344


MHC class I polypeptide-related sequence B plays a pivotal role in immune response regulation. It does not participate in antigen presentation but acts as a stress-induced self-antigen recognized by gamma delta T cells. It serves as a ligand for the KLRK1/NKG2D receptor, triggering cell lysis upon binding.

Therapeutic significance:

The protein's involvement in rheumatoid arthritis, an autoimmune disease affecting the joints, highlights its potential as a therapeutic target. Disease susceptibility is linked to genetic variants, including the MICB*004 allele, suggesting that modulating its activity could offer new treatment avenues.

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