Focused On-demand Library for Sialate O-acetylesterase

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

This extensive focused library is tailor-made using the latest virtual screening and parameter assessment technology, operated by the Receptor.AI drug discovery platform. This technique is more effective than traditional methods, offering compounds with improved 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.

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.

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

 Fig. 1. The sreening workflow of Receptor.AI

This approach involves comprehensive molecular simulations of the catalytic and allosteric binding pockets and ensemble virtual screening that accounts for their conformational flexibility. In the case of designing modulators, the structural adjustments caused by reaction intermediates are considered to improve activity and selectivity.

Our library distinguishes itself through several key aspects:

  • The Receptor.AI platform integrates all available data about the target protein, including past experiments, literature data, known ligands, structural information and more. This consolidated approach maximises the probability of prioritising highly relevant compounds.
  • The platform uses sophisticated molecular simulations to identify possible binding sites so that the compounds in the focused library are suitable for discovering allosteric inhibitors and the binders for cryptic pockets.
  • The platform integrates over 50 highly customisable AI models, which are thoroughly tested and validated on a multitude of commercial drug discovery programs and research projects. It is designed to be efficient, reliable and accurate. All this power is utilised when producing the focused libraries.
  • In addition to producing the focused libraries, Receptor.AI provides services and end-to-end solutions at every stage of preclinical drug discovery. The pricing model is success-based, which reduces your risks and leverages the mutual benefits of the project's success.







Alternative names:

H-Lse; Sialic acid-specific 9-O-acetylesterase

Alternative UPACC:

Q9HAT2; B3KPB0; Q8IUT9; Q9HAU7; Q9NT71


Sialate O-acetylesterase, also known as H-Lse or Sialic acid-specific 9-O-acetylesterase, plays a crucial role in modulating the sialic acid content on glycoproteins and glycolipids. This enzyme specifically catalyzes the removal of O-acetyl ester groups from the 9th position of the parent sialic acid, N-acetylneuraminic acid, a modification that significantly impacts the biological functions of sialic acids.

Therapeutic significance:

Given its involvement in autoimmune disease type 6, which encompasses conditions such as juvenile idiopathic arthritis, rheumatoid arthritis, multiple sclerosis, and type 1 diabetes, Sialate O-acetylesterase represents a promising target for therapeutic intervention. Understanding the role of Sialate O-acetylesterase could open doors to potential therapeutic strategies aimed at modulating immune responses and treating autoimmune disorders.

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