Focused On-demand Library for Fibroblast growth factor receptor 3

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.

We pick out particular compounds from an extensive virtual database of more than 60 billion molecules. The preparation and shipment of these compounds are facilitated by our associate Reaxense.

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 employ our advanced, specialised process to create targeted libraries for enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

This approach involves comprehensive molecular simulations of the catalytic and allosteric binding pockets and ensemble virtual screening that accounts for their conformational flexibility. In the case of designing modulators, the structural adjustments caused by reaction intermediates are considered to improve activity and selectivity.

Our library is unique due to several crucial aspects:

  • Receptor.AI compiles all relevant data on the target protein, such as past experimental results, literature findings, known ligands, and structural data, thereby enhancing the likelihood of focusing on the most significant compounds.
  • By utilizing advanced molecular simulations, the platform is adept at locating potential binding sites, rendering the compounds in the focused library well-suited for unearthing allosteric inhibitors and binders for hidden pockets.
  • The platform is supported by more than 50 highly specialized AI models, all of which have been rigorously tested and validated in diverse drug discovery and research programs. Its design emphasizes efficiency, reliability, and accuracy, crucial for producing focused libraries.
  • Receptor.AI extends beyond just creating focused libraries; it offers a complete spectrum of services and solutions during the preclinical drug discovery phase, with a success-dependent pricing strategy that reduces risk and fosters shared success in the project.







Alternative names:


Alternative UPACC:

P22607; D3DVP9; D3DVQ0; Q14308; Q16294; Q16608; Q59FL9


Fibroblast growth factor receptor 3 (FGFR3) is a critical tyrosine-protein kinase, pivotal in cell proliferation, differentiation, and apoptosis. It plays a key role in chondrocyte differentiation and skeletal development, influencing both osteogenesis and bone mineralization postnatally. FGFR3's involvement extends to the inner ear's development, with its activation affecting various signaling cascades essential for cellular function.

Therapeutic significance:

FGFR3 mutations are linked to a spectrum of skeletal disorders, including Achondroplasia and Thanatophoric Dysplasia, and cancers like bladder and cervical cancer. Understanding FGFR3's role could unveil new therapeutic strategies, particularly as its overexpression or constitutive activation is associated with disease pathogenesis. Targeting FGFR3 signaling pathways offers a promising avenue for treating conditions ranging from skeletal dysplasias to malignancies.

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