Focused On-demand Libraries - Receptor.AI 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 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 Reaxense.


The library includes a list of the most effective modulators, each annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Furthermore, each compound is shown with its optimal docking poses, affinity scores, and activity scores, offering a detailed summary.


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


 

Fig. 1. The screening 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.


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
Q9BZZ2

UPID:
SN_HUMAN

ALTERNATIVE NAMES:
Sialic acid-binding Ig-like lectin 1

ALTERNATIVE UPACC:
Q9BZZ2; Q96DL4; Q9GZS5; Q9H1H6; Q9H1H7; Q9H7L7

BACKGROUND:
Sialoadhesin, identified by its alternative name Sialic acid-binding Ig-like lectin 1, is crucial in limiting bacterial spread and enhancing macrophage-to-T-cell transmission of viruses such as HIV-1 and SARS-CoV-2. It mediates sialic-acid dependent interactions, playing a significant role in phagocytosis and antigen presentation. Additionally, Sialoadhesin is involved in the inhibition of antiviral innate immunity by promoting TBK1 degradation, highlighting its complex role in immune modulation.

THERAPEUTIC SIGNIFICANCE:
Exploring the multifaceted functions of Sialoadhesin offers a promising avenue for developing novel therapeutic interventions aimed at improving immune responses against bacterial and viral pathogens.

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