AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Immunoglobulin lambda constant 3

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.

From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Our collaborator, Reaxense, aids in their synthesis and provision.

The library features a range of promising modulators, each detailed with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Plus, each compound is presented with its ideal docking poses, affinity scores, and activity scores, ensuring a thorough insight.

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

 Fig. 1. The sreening 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

Reaxense

upacc

P0DOY3

UPID:

IGLC3_HUMAN

Alternative names:

Ig lambda chain C region DOT; Ig lambda chain C region NEWM; Ig lambda-3 chain C regions

Alternative UPACC:

P0DOY3; A0A075B6L0; A0M8Q4; P0CG05; P0CG06; P80423

Background:

Immunoglobulin lambda constant 3 (Ig lambda chain C region) plays a pivotal role in the immune response. It is part of the immunoglobulin light chains, crucial for the production of antibodies by B lymphocytes. These antibodies are either membrane-bound or secreted, engaging in the recognition and elimination of antigens. The antigen binding site, combining the variable domains of one heavy and one light chain, exhibits high affinity for specific antigens. This specificity is achieved through V-(D)-J rearrangement and somatic hypermutations, enhancing antigen affinity.

Therapeutic significance:

Understanding the role of Immunoglobulin lambda constant 3 could open doors to potential therapeutic strategies. Its critical function in humoral immunity highlights its importance in developing treatments for immune-related disorders.

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