Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

This comprehensive focused library is produced on demand with state-of-the-art virtual screening and parameter assessment technology driven by Receptor.AI drug discovery platform. This approach outperforms traditional methods and provides higher-quality compounds with superior activity, selectivity and safety.


The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is facilitated by Reaxense.


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.


Our high-tech, dedicated method is applied to construct targeted libraries.


 

Fig. 1. The screening 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.


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
Receptor.AI
 
UPACC
Q15485

UPID:
FCN2_HUMAN

ALTERNATIVE NAMES:
37 kDa elastin-binding protein; Collagen/fibrinogen domain-containing protein 2; EBP-37; Ficolin-B; Ficolin-beta; Hucolin; L-ficolin; Serum lectin p35

ALTERNATIVE UPACC:
Q15485; A6NFG7; A8K478; Q6IS69; Q7M4P4; Q9UC57

BACKGROUND:
Ficolin-2, known for its alternative names such as EBP-37 and Hucolin, plays a crucial role in the body's first line of defense. It functions as a calcium-dependent lectin that binds to GlcNAc, enhancing neutrophils' ability to phagocytose S.typhimurium. This action suggests an opsonic effect, crucial for the lectin complement pathway's activation and innate immunity support.

THERAPEUTIC SIGNIFICANCE:
The exploration of Ficolin-2's function in innate immunity and its opsonic effects provides a promising avenue for therapeutic intervention. Its critical role in enhancing phagocytosis and activating the lectin complement pathway could be pivotal in devising new treatments for bacterial infections.

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