Focused On-demand Library for Secreted frizzled-related protein 3

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.

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 includes a list of the most promising modulators annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Also, each compound is presented with its optimal docking poses, affinity scores, and activity scores, providing a comprehensive overview.

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

 Fig. 1. The sreening workflow of Receptor.AI

This process includes extensive molecular simulations of the receptor in its native membrane environment, along with ensemble virtual screening that accounts for its conformational mobility. In the case of dimeric or oligomeric receptors, the entire functional complex is modelled, identifying potential binding pockets on and between the subunits to encompass all possible mechanisms of action.

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.







Alternative names:

Frezzled; Fritz; Frizzled-related protein 1; FrzB-1

Alternative UPACC:

Q92765; O00181; Q99686


Secreted frizzled-related protein 3 (SFRP3), also known by alternative names such as Frezzled, Fritz, and Frizzled-related protein 1, plays a pivotal role in Wnt signaling modulation. It directly interacts with Wnts, influencing cell growth and differentiation in specific cell types. SFRP3 is crucial in limb skeletogenesis and acts as an antagonist of Wnt8 signaling, guiding chondrocyte maturation and long bone development.

Therapeutic significance:

SFRP3's involvement in osteoarthritis 1, a degenerative joint disease, highlights its potential as a therapeutic target. Disease susceptibility linked to gene variants affecting SFRP3 underscores the importance of understanding its function for developing treatments aimed at alleviating pain, stiffness, and disability associated with osteoarthritis.

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