Focused On-demand Library for Dihydropteridine reductase

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.

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 top-notch dedicated system is used to design specialised libraries for enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

It includes comprehensive molecular simulations of the catalytic and allosteric binding pockets and the ensemble virtual screening accounting for their conformational mobility. In the case of designing modulators, the structural changes induced by reaction intermediates are taken into account to leverage 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.







Alternative names:

HDHPR; Quinoid dihydropteridine reductase; Short chain dehydrogenase/reductase family 33C member 1

Alternative UPACC:

P09417; A8K158; B3KW71; Q53F52; Q9H3M5


Dihydropteridine reductase, known by its alternative names HDHPR and Quinoid dihydropteridine reductase, plays a crucial role in the conversion of quinonoid dihydrobiopterin into tetrahydrobiopterin. This enzyme is essential for the biosynthesis of neurotransmitters, including dopamine and serotonin, through its action in the phenylalanine metabolism pathway.

Therapeutic significance:

Hyperphenylalaninemia, BH4-deficient, C, a rare autosomal recessive disorder, is directly linked to mutations affecting dihydropteridine reductase. The disease manifests with severe neurological symptoms due to neurotransmitter depletion. Understanding the role of dihydropteridine reductase could open doors to potential therapeutic strategies, offering hope for targeted treatments.

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