AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for EvC complex member EVC

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.

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

The library features a range of promising modulators, each detailed with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Plus, each compound is presented with its ideal docking poses, affinity scores, and activity scores, ensuring a thorough insight.

We use our state-of-the-art dedicated workflow for designing 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.

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.

partner

Reaxense

upacc

P57679

UPID:

EVC_HUMAN

Alternative names:

DWF-1; Ellis-van Creveld syndrome protein

Alternative UPACC:

P57679

Background:

The EvC complex member EVC, also known as DWF-1 or Ellis-van Creveld syndrome protein, plays a crucial role in the regulation of ciliary Hedgehog signaling, pivotal for endochondral growth and skeletal development. This protein's involvement in the EvC complex underscores its significance in cellular signaling pathways that dictate skeletal formation and growth.

Therapeutic significance:

Mutations in the EVC protein are directly linked to Ellis-van Creveld syndrome and Acrofacial dysostosis, Weyers type, highlighting its critical role in skeletal anomalies and cardiac defects. Understanding the EVC protein's function offers a promising avenue for developing targeted therapies for these genetic disorders, potentially mitigating their impact on affected individuals.

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