AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Sialoadhesin

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.

From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Our collaborator, Reaxense, aids in their synthesis and provision.

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 utilise our cutting-edge, exclusive workflow to develop focused 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.

Key features that set our library apart include:

  • The Receptor.AI platform integrates extensive information about the target protein, such as historical experiments, academic research, known ligands, and structural insights, thereby increasing the likelihood of identifying highly relevant compounds.
  • The platform’s sophisticated molecular simulations are designed to discover potential binding sites, ensuring that our focused library is optimal for the discovery of allosteric inhibitors and binders for cryptic pockets.
  • With over 50 customisable AI models, verified through extensive testing in commercial drug discovery and research, Receptor.AI is efficient, reliable, and precise. These models are essential in the production of our focused libraries.
  • Receptor.AI not only produces focused libraries but also provides full services and solutions at every stage of preclinical drug discovery, with a success-based pricing structure that aligns our interests with the success of your project.

partner

Reaxense

upacc

Q9BZZ2

UPID:

SN_HUMAN

Alternative names:

Sialic acid-binding Ig-like lectin 1

Alternative UPACC:

Q9BZZ2; Q96DL4; Q9GZS5; Q9H1H6; Q9H1H7; Q9H7L7

Background:

Sialoadhesin, also known as Sialic acid-binding Ig-like lectin 1, is a macrophage-restricted adhesion molecule. It mediates sialic-acid dependent binding to various lymphocytes and plays a pivotal role in bacterial containment and viral infections management by promoting phagocytosis and antigen presentation. Sialoadhesin binds preferentially to alpha-2,3-linked sialic acid and facilitates viral entry into cells, including HIV-1 and SARS-CoV-2, by recognizing sialylated gangliosides on viral membranes.

Therapeutic significance:

Understanding the role of Sialoadhesin could open doors to potential therapeutic strategies, especially in enhancing immune response to bacterial dissemination and viral infections.

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