AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Low affinity immunoglobulin gamma Fc region receptor II-b

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.

We pick out particular compounds from an extensive virtual database of more than 60 billion molecules. The preparation and shipment of these compounds are facilitated by our associate Reaxense.

In the library, a selection of top modulators is provided, each marked with 38 ADME-Tox and 32 parameters related to physicochemical properties and drug-likeness. Also, every compound comes with its best docking poses, affinity scores, and activity scores, providing a comprehensive overview.

We use our state-of-the-art dedicated workflow for designing focused 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 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

P31994

UPID:

FCG2B_HUMAN

Alternative names:

CDw32; Fc-gamma RII-b

Alternative UPACC:

P31994; A6H8N3; O95649; Q53X85; Q5VXA9; Q8NIA1

Background:

The Low affinity immunoglobulin gamma Fc region receptor II-b, also known as CDw32 and Fc-gamma RII-b, plays a crucial role in the immune system. It acts as a receptor for the Fc region of complexed or aggregated immunoglobulins gamma, engaging in various effector and regulatory functions including phagocytosis of immune complexes and modulation of antibody production by B-cells. Its activity influences the modulation of cell activation states, impacting B-cell, T-cell, and Fc receptor-mediated responses.

Therapeutic significance:

Given its involvement in systemic lupus erythematosus, a disorder characterized by autoimmune system failure affecting multiple organs, the receptor presents a promising target for therapeutic intervention. Understanding the role of Low affinity immunoglobulin gamma Fc region receptor II-b could open doors to potential therapeutic strategies, offering hope for improved management of this complex disease.

Looking for more information on this library or underlying technology? Fill out the form below and we'll be in touch with all the details you need.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.