AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Estrogen receptor

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

Our detailed focused library is generated on demand with advanced virtual screening and parameter assessment technology powered by the Receptor.AI drug discovery platform. This method surpasses traditional approaches, delivering compounds of better quality with enhanced 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 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 for receptors.

 Fig. 1. The sreening workflow of Receptor.AI

This includes comprehensive molecular simulations of the receptor in its native membrane environment, paired with ensemble virtual screening that factors in its conformational mobility. In cases involving dimeric or oligomeric receptors, the entire functional complex is modelled, pinpointing potential binding pockets on and between the subunits to capture the full range of mechanisms of action.

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

P03372

UPID:

ESR1_HUMAN

Alternative names:

ER-alpha; Estradiol receptor; Nuclear receptor subfamily 3 group A member 1

Alternative UPACC:

P03372; Q13511; Q14276; Q5T5H7; Q6MZQ9; Q9NU51; Q9UDZ7; Q9UIS7

Background:

The Estrogen Receptor (ER-alpha), also known as Estradiol receptor or Nuclear receptor subfamily 3 group A member 1, plays a pivotal role in regulating gene expression and affecting cellular proliferation and differentiation. It functions through ligand-dependent nuclear transactivation, involving direct binding or association with other transcription factors, and mediates both genomic and non-genomic signaling pathways.

Therapeutic significance:

Estrogen resistance, a disorder characterized by resistance to estrogens despite elevated levels, implicates ER-alpha in its pathology. Understanding the role of ER-alpha could lead to novel therapeutic strategies for managing estrogen resistance and its associated metabolic complications.

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