AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for EGF-containing fibulin-like extracellular matrix protein 2

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 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.

We utilise our cutting-edge, exclusive workflow to develop focused libraries.

 Fig. 1. The sreening workflow of Receptor.AI

Our strategy employs molecular simulations to explore an extensive range of proteins, capturing their dynamics both individually and within complexes with other proteins. Through ensemble virtual screening, we address proteins' conformational mobility, uncovering key binding sites at both functional regions and remote allosteric locations. This comprehensive investigation ensures a thorough assessment of all potential mechanisms of action, with the goal of discovering innovative therapeutic targets and lead molecules across across diverse biological functions.

Several key aspects differentiate our library:

  • Receptor.AI compiles an all-encompassing dataset on the target protein, including historical experiments, literature data, known ligands, and structural insights, maximising the chances of prioritising the most pertinent compounds.
  • The platform employs state-of-the-art molecular simulations to identify potential binding sites, ensuring the focused library is primed for discovering allosteric inhibitors and binders of concealed pockets.
  • Over 50 customisable AI models, thoroughly evaluated in various drug discovery endeavours and research projects, make Receptor.AI both efficient and accurate. This technology is integral to the development of our focused libraries.
  • In addition to generating focused libraries, Receptor.AI offers a full range of services and solutions for every step of preclinical drug discovery, with a pricing model based on success, thereby reducing risk and promoting joint project success.

partner

Reaxense

upacc

O95967

UPID:

FBLN4_HUMAN

Alternative names:

Fibulin-4; Protein UPH1

Alternative UPACC:

O95967; A8K7R4; B3KM31; B3KQT1; O75967

Background:

EGF-containing fibulin-like extracellular matrix protein 2, also known as Fibulin-4 and Protein UPH1, is pivotal in elastic fiber formation and collagen fibril assembly in tissues. It ensures the integrity of the aorta's wall by forming ultrastructural connections between elastic laminae and smooth muscle cells, and regulates vascular smooth muscle cells proliferation via angiotensin signaling.

Therapeutic significance:

The protein's mutation is linked to Cutis laxa, autosomal recessive, 1B, a disorder affecting connective tissue elasticity and resilience. Understanding the role of EGF-containing fibulin-like extracellular matrix protein 2 could open doors to potential therapeutic strategies for treating connective tissue disorders and cardiovascular diseases.

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