AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for EGF-containing fibulin-like extracellular matrix protein 1

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

Our detailed focused library is generated on demand with advanced virtual screening and parameter assessment technology powered by the Receptor.AI drug discovery platform. This method surpasses traditional approaches, delivering compounds of better quality with enhanced 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 use our state-of-the-art dedicated workflow for designing focused 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.

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

Q12805

UPID:

FBLN3_HUMAN

Alternative names:

Extracellular protein S1-5; Fibrillin-like protein; Fibulin-3

Alternative UPACC:

Q12805; A8K3I4; B4DW75; D6W5D2; Q541U7

Background:

EGF-containing fibulin-like extracellular matrix protein 1, also known as Fibulin-3, plays a pivotal role in cellular processes including cell adhesion, migration, and differentiation. It binds to EGFR, inducing autophosphorylation and activation of downstream signaling pathways. Its involvement in the olfactory epithelium suggests a regulatory role in glial cell migration and differentiation.

Therapeutic significance:

Fibulin-3 is linked to Doyne honeycomb retinal dystrophy, a disease characterized by drusen deposits beneath the retinal pigment epithelium. Understanding Fibulin-3's role could pave the way for innovative treatments for this autosomal dominant disease.

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