AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Electron transfer flavoprotein subunit beta

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.

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.

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.

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

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

P38117

UPID:

ETFB_HUMAN

Alternative names:

-

Alternative UPACC:

P38117; A8K766; B3KNY2; Q6IBH7; Q71RF6; Q9Y3S7

Background:

Electron transfer flavoprotein subunit beta (ETF-beta) plays a pivotal role in mitochondrial energy metabolism, facilitating electron transfer from various dehydrogenases to the mitochondrial respiratory chain. This process is crucial for the oxidation of fatty acids and the metabolism of amino acids, ensuring cellular energy production.

Therapeutic significance:

Glutaric aciduria 2B, a metabolic disorder linked to ETF-beta, highlights the protein's critical role in fatty acid, amino acid, and choline metabolism. Understanding ETF-beta's function could lead to novel therapeutic strategies for managing this and related metabolic diseases.

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