Focused On-demand Library for Interleukin-17 receptor D

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.

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 top-notch dedicated system is used to design specialised libraries.

 Fig. 1. The sreening workflow of Receptor.AI

Our methodology leverages molecular simulations to examine a vast array of proteins, capturing their dynamics in both isolated forms and in complexes with other proteins. Through ensemble virtual screening, we thoroughly account for the protein's conformational mobility, identifying critical binding sites within functional regions and distant allosteric locations. This detailed exploration ensures that we comprehensively assess every possible mechanism of action, with the objective of identifying novel therapeutic targets and lead compounds that span a wide spectrum of biological functions.

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:

IL17Rhom; Interleukin-17 receptor-like protein; Sef homolog

Alternative UPACC:

Q8NFM7; Q2NKP7; Q58EZ7; Q6RVF4; Q6UWI5; Q8N113; Q8NFS0; Q9UFA0


Interleukin-17 receptor D (IL17RD), also known as Sef homolog and IL17Rhom, plays a critical role in regulating several signaling pathways. It acts as a feedback inhibitor of fibroblast growth factor (FGF) mediated Ras-MAPK signaling and ERK activation. IL17RD regulates nuclear ERK signaling by preventing the nuclear translocation of activated ERK. Additionally, it may mediate JNK activation and influence apoptosis, inhibit FGF-induced FGFR1 tyrosine phosphorylation, and play a role in the specification of GnRH-secreting neurons.

Therapeutic significance:

IL17RD's involvement in Hypogonadotropic hypogonadism 18 with or without anosmia highlights its potential as a therapeutic target. Understanding the role of IL17RD could open doors to potential therapeutic strategies for treating this disorder, which is characterized by low levels of circulating gonadotropins and testosterone, and in some cases, anosmia, cleft palate, and sensorineural hearing loss.

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