Focused On-demand Library for Peroxisomal trans-2-enoyl-CoA reductase

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

This extensive focused library is tailor-made using the latest virtual screening and parameter assessment technology, operated by the Receptor.AI drug discovery platform. This technique is more effective than traditional methods, offering compounds with improved activity, selectivity, and safety.

We pick out particular compounds from an extensive virtual database of more than 60 billion molecules. The preparation and shipment of these compounds are facilitated by our associate Reaxense.

In the library, a selection of top modulators is provided, each marked with 38 ADME-Tox and 32 parameters related to physicochemical properties and drug-likeness. Also, every compound comes with its best docking poses, affinity scores, and activity scores, providing a comprehensive overview.

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.

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:

2,4-dienoyl-CoA reductase-related protein; HPDHase; Short chain dehydrogenase/reductase family 29C member 1; pVI-ARL

Alternative UPACC:

Q9BY49; B2RE42; Q53TC4; Q6IAK9; Q9NRD4; Q9NY60; Q9P1A4


Peroxisomal trans-2-enoyl-CoA reductase, known by alternative names such as 2,4-dienoyl-CoA reductase-related protein, HPDHase, and Short chain dehydrogenase/reductase family 29C member 1, plays a crucial role in fatty acid metabolism. It specifically catalyzes the reduction of trans-2-enoyl-CoAs with chain lengths ranging from 6:1 to 16:1, exhibiting peak activity with 10:1 CoA. This enzyme is pivotal in the chain elongation process of fatty acids, a fundamental biochemical pathway.

Therapeutic significance:

Understanding the role of Peroxisomal trans-2-enoyl-CoA reductase could open doors to potential therapeutic strategies. Its critical function in fatty acid metabolism makes it a compelling target for research aimed at treating metabolic disorders.

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