Focused On-demand Library for Lysophospholipid acyltransferase 7

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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.

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.

The library includes a list of the most promising modulators annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Also, each compound is presented with its optimal 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 sreening workflow of Receptor.AI

This approach involves comprehensive molecular simulations of the catalytic and allosteric binding pockets and ensemble virtual screening that accounts for their conformational flexibility. In the case of designing modulators, the structural adjustments caused by reaction intermediates are considered to improve 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:

1-acylglycerophosphatidylinositol O-acyltransferase; Bladder and breast carcinoma-overexpressed gene 1 protein; Leukocyte receptor cluster member 4; Lysophosphatidylinositol acyltransferase; Membrane-bound O-acyltransferase domain-containing protein 7

Alternative UPACC:

Q96N66; A9C4B6; B0V3I5; B4DQ87; Q05DF0; Q7L5N2; Q99908; Q9BPV2; Q9BRE9


Lysophospholipid acyltransferase 7, known by alternative names such as 1-acylglycerophosphatidylinositol O-acyltransferase and Lysophosphatidylinositol acyltransferase, plays a crucial role in lipid metabolism. It catalyzes the transfer of acyl groups to lysophosphatidylinositol, contributing to phosphatidylinositol production and the regulation of arachidonic acid levels. This enzyme is pivotal in the Lands cycle, impacting cell membrane composition and function.

Therapeutic significance:

The enzyme's association with Intellectual developmental disorder, autosomal recessive 57, highlights its importance in neurological development and function. Understanding the role of Lysophospholipid acyltransferase 7 could open doors to potential therapeutic strategies for treating intellectual disabilities and related metabolic disorders.

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