Focused On-demand Library for Delta(14)-sterol reductase TM7SF2

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.

We carefully select specific compounds from a vast collection of over 60 billion molecules in virtual chemical space. Our partner Reaxense helps in synthesizing and delivering these compounds.

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.

We utilise our cutting-edge, exclusive workflow to develop focused 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.

Our library distinguishes itself through several key aspects:

  • The Receptor.AI platform integrates all available data about the target protein, including past experiments, literature data, known ligands, structural information and more. This consolidated approach maximises the probability of prioritising highly relevant compounds.
  • The platform uses sophisticated molecular simulations to identify possible binding sites so that the compounds in the focused library are suitable for discovering allosteric inhibitors and the binders for cryptic pockets.
  • The platform integrates over 50 highly customisable AI models, which are thoroughly tested and validated on a multitude of commercial drug discovery programs and research projects. It is designed to be efficient, reliable and accurate. All this power is utilised when producing the focused libraries.
  • In addition to producing the focused libraries, Receptor.AI provides services and end-to-end solutions at every stage of preclinical drug discovery. The pricing model is success-based, which reduces your risks and leverages the mutual benefits of the project's success.







Alternative names:

3-beta-hydroxysterol Delta (14)-reductase; Another new gene 1 protein; C-14 sterol reductase; Putative sterol reductase SR-1; Sterol C14-reductase; Transmembrane 7 superfamily member 2

Alternative UPACC:

O76062; A8K4H0; O95982; Q8IY06; Q96E64; Q96GZ1


Delta(14)-sterol reductase TM7SF2, known by alternative names such as 3-beta-hydroxysterol Delta(14)-reductase and Sterol C14-reductase, plays a crucial role in cholesterol biosynthesis. It catalyzes the reduction of the C14-unsaturated bond of lanosterol, a key step in the metabolic pathway leading to cholesterol production. This enzyme's activity is essential for maintaining cellular cholesterol levels and overall cell membrane integrity.

Therapeutic significance:

Understanding the role of Delta(14)-sterol reductase TM7SF2 could open doors to potential therapeutic strategies. Its pivotal function in cholesterol biosynthesis makes it a target for developing treatments aimed at regulating cholesterol levels, potentially addressing conditions related to cholesterol imbalance.

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