Focused On-demand Library for Calcium-independent phospholipase A2-gamma

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 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.

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 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:

Intracellular membrane-associated calcium-independent phospholipase A2 gamma; PNPLA-gamma; Patatin-like phospholipase domain-containing protein 8; iPLA2-2

Alternative UPACC:

Q9NP80; A4D0S1; C9JZI4; O95035; Q8N3I3; Q9H7T5; Q9NR17; Q9NUN2; Q9NZ79


Calcium-independent phospholipase A2-gamma (iPLA2-gamma), also known as PNPLA-gamma and Patatin-like phospholipase domain-containing protein 8, plays a crucial role in cellular lipid metabolism. It catalyzes the hydrolysis of fatty acids from glycerophospholipids, regulating membrane properties and signaling pathways through the production of free fatty acids and lysophospholipids. Its activity is pivotal in the mobilization of arachidonic acid, a key mediator in eicosanoid signaling, and in mitochondrial bioenergetics.

Therapeutic significance:

Given its involvement in mitochondrial myopathy with lactic acidosis, a disorder characterized by muscle weakness and neural deafness, targeting iPLA2-gamma presents a promising avenue for therapeutic intervention. Understanding the role of Calcium-independent phospholipase A2-gamma could open doors to potential therapeutic strategies.

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