Focused On-demand Library for C-type lectin domain family 7 member A

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.

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 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.

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

 Fig. 1. The sreening workflow of Receptor.AI

Our methodology leverages molecular simulations to examine a vast array of proteins, capturing their dynamics in both isolated forms and in complexes with other proteins. Through ensemble virtual screening, we thoroughly account for the protein's conformational mobility, identifying critical binding sites within functional regions and distant allosteric locations. This detailed exploration ensures that we comprehensively assess every possible mechanism of action, with the objective of identifying novel therapeutic targets and lead compounds that span a wide spectrum of biological functions.

Our library stands out due to several important features:

  • The Receptor.AI platform compiles comprehensive data on the target protein, encompassing previous experiments, literature, known ligands, structural details, and more, leading to a higher chance of selecting the most relevant compounds.
  • Advanced molecular simulations on the platform help pinpoint potential binding sites, making the compounds in our focused library ideal for finding allosteric inhibitors and targeting cryptic pockets.
  • Receptor.AI boasts over 50 tailor-made AI models, rigorously tested and proven in various drug discovery projects and research initiatives. They are crafted for efficacy, dependability, and precision, all of which are key in creating our focused libraries.
  • Beyond creating focused libraries, Receptor.AI offers comprehensive services and complete solutions throughout the preclinical drug discovery phase. Our success-based pricing model minimises risk and maximises the mutual benefits of the project's success.







Alternative names:

Beta-glucan receptor; C-type lectin superfamily member 12; Dendritic cell-associated C-type lectin 1

Alternative UPACC:

Q9BXN2; B2R861; B7Z494; B7Z5A9; B7Z5B9; Q6IPS7; Q96D32; Q96DR9; Q96LD3; Q96PA4; Q96PA5; Q96PA6; Q96PA7; Q96PA8; Q9H1K3


C-type lectin domain family 7 member A, also known as Beta-glucan receptor, plays a crucial role in the immune system. It recognizes specific patterns on pathogens, triggering an immune response. This protein is essential for the activation of inflammatory pathways, including NF-kappa-B and MAP kinase, and enhances cytokine production in immune cells. It also mediates phagocytosis of Candida albicans, a common fungal pathogen.

Therapeutic significance:

Given its pivotal role in fungal infections, particularly Candidiasis, familial, 4, understanding the function of C-type lectin domain family 7 member A could lead to novel therapeutic strategies. Targeting this protein may improve treatment outcomes for patients with immune deficiencies struggling with persistent fungal infections.

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.