Focused On-demand Libraries - Receptor.AI 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.


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


 

Fig. 1. The screening workflow of Receptor.AI

The procedure entails thorough molecular simulations of the catalytic and allosteric binding pockets, accompanied by ensemble virtual screening that factors in their conformational flexibility. When developing modulators, the structural modifications brought about by reaction intermediates are factored in to optimize 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.


PARTNER
Receptor.AI
 
UPACC
P21802

UPID:
FGFR2_HUMAN

ALTERNATIVE NAMES:
K-sam; Keratinocyte growth factor receptor

ALTERNATIVE UPACC:
P21802; B4DFC2; E7EVR6; E9PCR0; P18443; Q01742; Q12922; Q14300; Q14301; Q14302; Q14303; Q14304; Q14305; Q14672; Q14718; Q14719; Q1KHY5; Q86YI4; Q8IXC7; Q96KL9; Q96KM0; Q96KM1; Q96KM2; Q9NZU2; Q9NZU3; Q9UD01; Q9UD02; Q9UIH3; Q9UIH4; Q9UIH5; Q9UIH6; Q9UIH7; Q9UIH8; Q9UM87; Q9UMC6; Q9UNS7; Q9UQH7; Q9UQH8; Q9UQH9; Q9UQI0

BACKGROUND:
The protein Fibroblast growth factor receptor 2, with aliases K-sam and Keratinocyte growth factor receptor, plays a crucial role in regulating cell life cycles and embryonic development. It is essential for normal bone and skin formation, acting through phosphorylation of key molecules and activation of signaling pathways such as PLCG1, FRS2, and PAK4.

THERAPEUTIC SIGNIFICANCE:
Aberrant FGFR2 signaling, due to mutations, is implicated in several developmental syndromes, such as Pfeiffer, Beare-Stevenson, and Saethre-Chotzen syndromes. The exploration of FGFR2's role opens doors to potential therapeutic strategies, aiming to correct or mitigate the effects of these mutations.

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