Focused On-demand Library for Phospholipase B1, membrane-associated

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.

From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Our collaborator, Reaxense, aids in their synthesis and provision.

The library includes a list of the most effective modulators, each annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Furthermore, each compound is shown with its optimal docking poses, affinity scores, and activity scores, offering a detailed summary.

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

 Fig. 1. The sreening workflow of Receptor.AI

Our methodology employs molecular simulations to explore a wide array of proteins, capturing their dynamic states both individually and within complexes. Through ensemble virtual screening, we address conformational mobility, uncovering binding sites within functional regions and remote allosteric locations. This thorough exploration ensures no potential mechanism of action is overlooked, aiming to discover novel therapeutic targets and lead compounds across an extensive spectrum of biological functions.

Our library stands out due to several important features:

  • The Receptor.AI platform compiles comprehensive data on the target protein, encompassing previous experiments, literature, known ligands, structural details, and more, leading to a higher chance of selecting the most relevant compounds.
  • Advanced molecular simulations on the platform help pinpoint potential binding sites, making the compounds in our focused library ideal for finding allosteric inhibitors and targeting cryptic pockets.
  • Receptor.AI boasts over 50 tailor-made AI models, rigorously tested and proven in various drug discovery projects and research initiatives. They are crafted for efficacy, dependability, and precision, all of which are key in creating our focused libraries.
  • Beyond creating focused libraries, Receptor.AI offers comprehensive services and complete solutions throughout the preclinical drug discovery phase. Our success-based pricing model minimises risk and maximises the mutual benefits of the project's success.







Alternative names:

Lysophospholipase; Phospholipase A2; Phospholipase B/lipase; Triacylglycerol lipase

Alternative UPACC:

Q6P1J6; A8KAX2; Q53S03; Q8IUP7; Q96DP9


Phospholipase B1, membrane-associated, exhibits a broad spectrum of enzymatic activities, including phospholipase B, lysophospholipase, and lipase, targeting various glycerolipid and phospholipid substrates. This protein, identified by the accession number Q6P1J6, plays a pivotal role in lipid metabolism by hydrolyzing fatty acyl chains in phospholipids and glycerolipids, with a preference for sn-2 positions. Its alternative names, such as Lysophospholipase and Triacylglycerol lipase, reflect its diverse enzymatic functions.

Therapeutic significance:

Understanding the role of Phospholipase B1, membrane-associated, could open doors to potential therapeutic strategies.

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