AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Leiomodin-1

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 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 leverages molecular simulations to examine a vast array of proteins, capturing their dynamics in both isolated forms and in complexes with other proteins. Through ensemble virtual screening, we thoroughly account for the protein's conformational mobility, identifying critical binding sites within functional regions and distant allosteric locations. This detailed exploration ensures that we comprehensively assess every possible mechanism of action, with the objective of identifying novel therapeutic targets and lead compounds that span a wide spectrum of biological functions.

Key features that set our library apart include:

  • The Receptor.AI platform integrates extensive information about the target protein, such as historical experiments, academic research, known ligands, and structural insights, thereby increasing the likelihood of identifying highly relevant compounds.
  • The platform’s sophisticated molecular simulations are designed to discover potential binding sites, ensuring that our focused library is optimal for the discovery of allosteric inhibitors and binders for cryptic pockets.
  • With over 50 customisable AI models, verified through extensive testing in commercial drug discovery and research, Receptor.AI is efficient, reliable, and precise. These models are essential in the production of our focused libraries.
  • Receptor.AI not only produces focused libraries but also provides full services and solutions at every stage of preclinical drug discovery, with a success-based pricing structure that aligns our interests with the success of your project.

partner

Reaxense

upacc

P29536

UPID:

LMOD1_HUMAN

Alternative names:

64 kDa autoantigen 1D; 64 kDa autoantigen 1D3; 64 kDa autoantigen D1; Leiomodin, muscle form; Smooth muscle leiomodin; Thyroid-associated ophthalmopathy autoantigen

Alternative UPACC:

P29536; B1APV6; C4AMB1; Q68EN2

Background:

Leiomodin-1, also known as Smooth muscle leiomodin, plays a pivotal role in the contractility of visceral smooth muscle cells. It achieves this by mediating the nucleation of actin filaments, a process critical for muscle contraction. This protein is encoded by the gene with the accession number P29536 and is recognized by several alternative names, including 64 kDa autoantigen 1D and Thyroid-associated ophthalmopathy autoantigen.

Therapeutic significance:

Leiomodin-1 is implicated in Megacystis-microcolon-intestinal hypoperistalsis syndrome 3 (MMIHS3), a severe congenital disorder affecting smooth muscle function in the bladder and intestine. Understanding the role of Leiomodin-1 could open doors to potential therapeutic strategies for MMIHS3, a condition with a high mortality rate due to malnutrition, sepsis, and multiorgan failure.

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