Focused On-demand Libraries - Receptor.AI 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.


From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Reaxense aids in their synthesis and provision.


In the library, a selection of top modulators is provided, each marked with 38 ADME-Tox and 32 parameters related to physicochemical properties and drug-likeness. Also, every compound comes with its best docking poses, affinity scores, and activity scores, providing a comprehensive overview.


We employ our advanced, specialised process to create targeted libraries.


 

Fig. 1. The screening 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.


Our library stands out due to several important features:


  • The Receptor.AI platform compiles comprehensive data on the target protein, encompassing previous experiments, literature, known ligands, structural details, and more, leading to a higher chance of selecting the most relevant compounds.

  • Advanced molecular simulations on the platform help pinpoint potential binding sites, making the compounds in our focused library ideal for finding allosteric inhibitors and targeting cryptic pockets.

  • Receptor.AI boasts over 50 tailor-made AI models, rigorously tested and proven in various drug discovery projects and research initiatives. They are crafted for efficacy, dependability, and precision, all of which are key in creating our focused libraries.

  • Beyond creating focused libraries, Receptor.AI offers comprehensive services and complete solutions throughout the preclinical drug discovery phase. Our success-based pricing model minimises risk and maximises the mutual benefits of the project's success.


PARTNER
Receptor.AI
 
UPACC
P49765

UPID:
VEGFB_HUMAN

ALTERNATIVE NAMES:
VEGF-related factor

ALTERNATIVE UPACC:
P49765; Q16528

BACKGROUND:
The Vascular endothelial growth factor B, also known as VEGF-related factor, is essential for endothelial cell proliferation. It exists in different forms, with VEGF-B167 and VEGF-B186 being prominent for their unique binding capabilities. VEGF-B167's affinity for heparin and neuropilin-1, alongside VEGF-B186's regulated interaction with neuropilin-1, highlights the protein's significant role in endothelial signaling pathways.

THERAPEUTIC SIGNIFICANCE:
Exploring the functionalities of Vascular endothelial growth factor B unveils potential avenues for therapeutic intervention. Its fundamental involvement in endothelial cell growth suggests its utility in crafting novel therapies for diseases linked to endothelial dysfunction and abnormal vascular growth.

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