Focused On-demand Library for Immunoglobulin lambda variable 2-14

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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.

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.

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.

We utilise our cutting-edge, exclusive workflow to develop focused 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.

Key features that set our library apart include:

  • The Receptor.AI platform integrates extensive information about the target protein, such as historical experiments, academic research, known ligands, and structural insights, thereby increasing the likelihood of identifying highly relevant compounds.
  • The platform’s sophisticated molecular simulations are designed to discover potential binding sites, ensuring that our focused library is optimal for the discovery of allosteric inhibitors and binders for cryptic pockets.
  • With over 50 customisable AI models, verified through extensive testing in commercial drug discovery and research, Receptor.AI is efficient, reliable, and precise. These models are essential in the production of our focused libraries.
  • Receptor.AI not only produces focused libraries but also provides full services and solutions at every stage of preclinical drug discovery, with a success-based pricing structure that aligns our interests with the success of your project.







Alternative names:

Ig lambda chain V-II region NIG-84; Ig lambda chain V-II region TOG; Ig lambda chain V-II region VIL

Alternative UPACC:

P01704; A0A075B6K1; P01711; P04209


Immunoglobulin lambda variable 2-14, known by alternative names such as Ig lambda chain V-II region NIG-84, TOG, and VIL, plays a pivotal role in the immune response. It is part of the variable domain of immunoglobulin light chains, crucial for antigen recognition. These immunoglobulins, produced by B lymphocytes, serve as both receptors on the cell surface and secreted antibodies, mediating the elimination of antigens through high-affinity binding sites formed by the variable domains of heavy and light chains.

Therapeutic significance:

Understanding the role of Immunoglobulin lambda variable 2-14 could open doors to potential therapeutic strategies. Its involvement in the antigen recognition and elimination process highlights its importance in humoral immunity, suggesting avenues for enhancing vaccine efficacy and developing targeted immunotherapies.

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