Focused On-demand Library for 2,4-dienoyl-CoA reductase [(3E)-enoyl-CoA-producing], mitochondrial

Focused On-demand Libraries - Receptor.AI 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. Reaxense aids in their synthesis and provision.


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 high-tech, dedicated method is applied to construct targeted libraries for enzymes.


 

Fig. 1. The screening workflow of Receptor.AI

It includes in-depth molecular simulations of both the catalytic and allosteric binding pockets, with ensemble virtual screening focusing on their conformational flexibility. For modulators, the process includes considering the structural shifts due to reaction intermediates to boost 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.

PARTNER
Receptor.AI
 
UPACC
Q16698

UPID:
DECR_HUMAN

ALTERNATIVE NAMES:
2,4-dienoyl-CoA reductase [NADPH]; Short chain dehydrogenase/reductase family 18C member 1

ALTERNATIVE UPACC:
Q16698; B7Z6B8; Q2M304; Q93085

BACKGROUND:
2,4-dienoyl-CoA reductase, pivotal in mitochondrial fatty acid metabolism, ensures the efficient breakdown of polyunsaturated fatty acids. This enzyme, also recognized as 2,4-dienoyl-CoA reductase [NADPH], is instrumental in converting 2,4-dienoyl-CoA to trans-3-enoyl-CoA, a step critical for energy extraction from fats.

THERAPEUTIC SIGNIFICANCE:
Its involvement in 2,4-dienoyl-CoA reductase deficiency highlights the enzyme's potential as a target for therapeutic intervention. The exploration of its functions and mechanisms could lead to breakthroughs in treating metabolic and mitochondrial disorders, underscoring the enzyme's therapeutic significance.

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