AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Beta-galactosidase-1-like protein 3

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.

Our selection of compounds is from a large virtual library of over 60 billion molecules. The production and distribution of these compounds are managed by our partner 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 for enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

The method includes detailed molecular simulations of the catalytic and allosteric binding pockets, along with ensemble virtual screening that considers their conformational flexibility. In the design of modulators, structural changes induced by reaction intermediates are taken into account to enhance activity and selectivity.

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

Reaxense

upacc

Q8NCI6

UPID:

GLBL3_HUMAN

Alternative names:

-

Alternative UPACC:

Q8NCI6; A6NEM0; A6NN15; Q6P3S3; Q96FF8

Background:

Beta-galactosidase-1-like protein 3, encoded by the gene with the accession number Q8NCI6, plays a crucial role in cellular processes. Despite its name suggesting a similarity to beta-galactosidase, its specific functions and mechanisms of action in biological systems are subjects of ongoing research. This protein's structure and biochemical pathways, while not fully elucidated, are believed to be significant in maintaining cellular health.

Therapeutic significance:

Understanding the role of Beta-galactosidase-1-like protein 3 could open doors to potential therapeutic strategies. The exploration of its functions and interactions within the cell promises to uncover novel targets for drug development, particularly in diseases where its activity is dysregulated.

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