Focused On-demand Library for Vascular endothelial growth factor A, long form

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

The specialised, focused library is developed on demand with the most recent virtual screening and parameter assessment technology, guided by the Receptor.AI drug discovery platform. This approach exceeds the capabilities of traditional methods and offers compounds with higher activity, selectivity, and safety.

The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is facilitated by our partner Reaxense.

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.

Our top-notch dedicated system is used to design specialised libraries for receptors.

 Fig. 1. The sreening workflow of Receptor.AI

It includes extensive molecular simulations of the receptor in its native membrane environment and the ensemble virtual screening accounting for its conformational mobility. In the case of dimeric or oligomeric receptors, the whole functional complex is modelled, and the tentative binding pockets are determined on and between the subunits to cover the whole spectrum of possible mechanisms of action.

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.







Alternative names:

Vascular permeability factor

Alternative UPACC:

P15692; B5BU86; H0Y2S8; H0Y407; H0Y414; H0Y462; H0Y8N2; H3BLW7; O60720; O75875; Q074Z4; Q16889; Q5UB46; Q6P0P5; Q96KJ0; Q96L82; Q96NW5; Q9H1W8; Q9H1W9; Q9UH58; Q9UL23


Vascular endothelial growth factor A, long form, also known as Vascular permeability factor, plays a pivotal role in angiogenesis, vasculogenesis, and endothelial cell growth. It activates key pathways by binding to receptors such as FLT1/VEGFR1, KDR/VEGFR2, and NRP1/neuropilin-1, promoting cell migration, proliferation, and survival while inhibiting apoptosis.

Therapeutic significance:

Given its involvement in microvascular complications of diabetes, including diabetic retinopathy, nephropathy, and neuropathy, understanding the role of Vascular endothelial growth factor A could open doors to potential therapeutic strategies for managing diabetes-related vascular issues.

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