AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Immunoglobulin lambda constant 2

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

The focused library is created on demand with the latest virtual screening and parameter assessment technology, supported by the Receptor.AI drug discovery platform. This method is more effective than traditional methods and results in higher-quality compounds with better 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.

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

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.

Our library is unique due to several crucial aspects:

  • Receptor.AI compiles all relevant data on the target protein, such as past experimental results, literature findings, known ligands, and structural data, thereby enhancing the likelihood of focusing on the most significant compounds.
  • By utilizing advanced molecular simulations, the platform is adept at locating potential binding sites, rendering the compounds in the focused library well-suited for unearthing allosteric inhibitors and binders for hidden pockets.
  • The platform is supported by more than 50 highly specialized AI models, all of which have been rigorously tested and validated in diverse drug discovery and research programs. Its design emphasizes efficiency, reliability, and accuracy, crucial for producing focused libraries.
  • Receptor.AI extends beyond just creating focused libraries; it offers a complete spectrum of services and solutions during the preclinical drug discovery phase, with a success-dependent pricing strategy that reduces risk and fosters shared success in the project.

partner

Reaxense

upacc

P0DOY2

UPID:

IGLC2_HUMAN

Alternative names:

Ig lambda chain C region Kern; Ig lambda chain C region NIG-64; Ig lambda chain C region SH; Ig lambda chain C region X; Ig lambda-2 chain C region

Alternative UPACC:

P0DOY2; A0A075B6K9; A0M8Q4; P0CG05; P0CG06; P80423

Background:

Immunoglobulin lambda constant 2, known by alternative names such as Ig lambda chain C region Kern and Ig lambda-2 chain C region, plays a pivotal role in humoral immunity. It is part of the constant region of immunoglobulin light chains, crucial for the production of antibodies by B lymphocytes. These antibodies can be either membrane-bound or secreted, engaging in the recognition and elimination of antigens through a highly specific binding site formed by the variable domains of heavy and light chains.

Therapeutic significance:

Understanding the role of Immunoglobulin lambda constant 2 could open doors to potential therapeutic strategies. Its involvement in the production and function of antibodies highlights its significance in immune response and disease prevention.

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