Focused On-demand Library for 17-beta-hydroxysteroid dehydrogenase 14

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.

We employ our advanced, specialised process to create targeted libraries for enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

The procedure entails thorough molecular simulations of the catalytic and allosteric binding pockets, accompanied by ensemble virtual screening that factors in their conformational flexibility. When developing modulators, the structural modifications brought about by reaction intermediates are factored in to optimize activity and selectivity.

Our library is unique due to several crucial aspects:

  • Receptor.AI compiles all relevant data on the target protein, such as past experimental results, literature findings, known ligands, and structural data, thereby enhancing the likelihood of focusing on the most significant compounds.
  • By utilizing advanced molecular simulations, the platform is adept at locating potential binding sites, rendering the compounds in the focused library well-suited for unearthing allosteric inhibitors and binders for hidden pockets.
  • The platform is supported by more than 50 highly specialized AI models, all of which have been rigorously tested and validated in diverse drug discovery and research programs. Its design emphasizes efficiency, reliability, and accuracy, crucial for producing focused libraries.
  • Receptor.AI extends beyond just creating focused libraries; it offers a complete spectrum of services and solutions during the preclinical drug discovery phase, with a success-dependent pricing strategy that reduces risk and fosters shared success in the project.







Alternative names:

17-beta-hydroxysteroid dehydrogenase DHRS10; Dehydrogenase/reductase SDR family member 10; Retinal short-chain dehydrogenase/reductase retSDR3; Short chain dehydrogenase/reductase family 47C member 1

Alternative UPACC:



17-beta-hydroxysteroid dehydrogenase 14, also known as DHRS10, plays a crucial role in steroid metabolism by converting oestradiol to oestrone. This enzyme, part of the short chain dehydrogenase/reductase family, exhibits NAD-dependent activity, highlighting its importance in biochemical pathways. Alternative names include Dehydrogenase/reductase SDR family member 10 and Retinal short-chain dehydrogenase/reductase retSDR3.

Therapeutic significance:

Understanding the role of 17-beta-hydroxysteroid dehydrogenase 14 could open doors to potential therapeutic strategies. Its involvement in steroid metabolism makes it a compelling target for drug discovery, aiming to modulate hormonal balances in diseases.

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