Focused On-demand Library for Low-density lipoprotein receptor-related protein 4

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.

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 top-notch dedicated system is used to design specialised 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.

partner

Reaxense

upacc

O75096

UPID:

LRP4_HUMAN

Alternative names:

Multiple epidermal growth factor-like domains 7

Alternative UPACC:

O75096; B2RN39; Q4AC85; Q5KTZ5

Background:

Low-density lipoprotein receptor-related protein 4 (LRP4), also known as Multiple epidermal growth factor-like domains 7, plays a pivotal role in bone formation and neuromuscular junction maintenance. It facilitates SOST-mediated inhibition of Wnt signaling and is crucial for digit differentiation and AGRIN-induced MUSK phosphorylation.

Therapeutic significance:

LRP4's involvement in Cenani-Lenz syndactyly syndrome, Sclerosteosis 2, and congenital myasthenic syndrome 17 highlights its potential as a therapeutic target. Understanding LRP4's functions could lead to innovative treatments for these genetic disorders.