AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Sialic acid-binding Ig-like lectin 10

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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.

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

Q96LC7

UPID:

SIG10_HUMAN

Alternative names:

Siglec-like protein 2

Alternative UPACC:

Q96LC7; A8K1I5; A8K3C7; C9JJ33; C9JM10; F8W917; Q3MIR5; Q6UXI8; Q96G54; Q96LC8

Background:

Sialic acid-binding Ig-like lectin 10 (Siglec-10) is a putative adhesion molecule that mediates sialic-acid dependent binding to cells, with a preference for alpha-2,3- or alpha-2,6-linked sialic acid. It plays a crucial role in the immune response by acting as an inhibitory receptor, recruiting cytoplasmic phosphatases that block signal transduction through dephosphorylation. Siglec-10 is involved in the negative regulation of B-cell antigen receptor signaling and controls autoimmunity.

Therapeutic significance:

Understanding the role of Sialic acid-binding Ig-like lectin 10 could open doors to potential therapeutic strategies.

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