Focused On-demand Library for Diacylglycerol lipase-beta

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.

The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is facilitated by our partner 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.

We use our state-of-the-art dedicated workflow for designing focused 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 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:

KCCR13L; PUFA-specific triacylglycerol lipase; Sn1-specific diacylglycerol lipase beta

Alternative UPACC:

Q8NCG7; A4D2P3; B3KV90; B4DQU0; Q6PIX3; Q8N2N2; Q8N9S1; Q8TED3; Q8WXE6


Diacylglycerol lipase-beta (DAGL-beta), known by alternative names such as KCCR13L and PUFA-specific triacylglycerol lipase, is a pivotal enzyme in lipid metabolism. It catalyzes the hydrolysis of arachidonic acid-esterified diacylglycerols to produce 2-arachidonoylglycerol (2-AG), a principal endocannabinoid. This enzyme preferentially targets DAGs at the sn-1 position in a calcium-dependent manner, playing a crucial role in regulating 2-AG and arachidonic acid pools for eicosanoid production via cyclooxygenase pathways.

Therapeutic significance:

Understanding the role of Diacylglycerol lipase-beta could open doors to potential therapeutic strategies, particularly in modulating inflammatory responses and lipid mediator production.

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