Focused On-demand Library for Fibroblast growth factor 13

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

This comprehensive focused library is produced on demand with state-of-the-art virtual screening and parameter assessment technology driven by Receptor.AI drug discovery platform. This approach outperforms traditional methods and provides higher-quality compounds with superior 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.

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.

Our high-tech, dedicated method is applied to construct targeted 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.

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.







Alternative names:

Fibroblast growth factor homologous factor 2

Alternative UPACC:

Q92913; B1AK18; B7Z4M7; B7Z8N0; D3DWH4; O95830; Q9NZH9; Q9NZI0


Fibroblast growth factor 13 (FGF13), also known as a Fibroblast growth factor homologous factor 2, plays a pivotal role in the nervous system. It binds directly to tubulin, influencing microtubule polymerization and stabilization. This action is crucial for neuron polarization, migration in the cerebral cortex, and hippocampus. FGF13 is instrumental in regulating voltage-gated sodium channel transport and function, impacting neuronal excitability and signaling.

Therapeutic significance:

FGF13's involvement in developmental and epileptic encephalopathy 90 and intellectual developmental disorder, X-linked 110, underscores its potential as a therapeutic target. Understanding FGF13's role could open doors to novel strategies for treating these neurological disorders, offering hope for improved outcomes.

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