Focused On-demand Libraries - Receptor.AI 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.


Our selection of compounds is from a large virtual library of over 60 billion molecules. The production and distribution of these compounds are managed by Reaxense.


In the library, a selection of top modulators is provided, each marked with 38 ADME-Tox and 32 parameters related to physicochemical properties and drug-likeness. Also, every compound comes with its best docking poses, affinity scores, and activity scores, providing a comprehensive overview.


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


 

Fig. 1. The screening workflow of Receptor.AI

Our methodology employs molecular simulations to explore a wide array of proteins, capturing their dynamic states both individually and within complexes. Through ensemble virtual screening, we address conformational mobility, uncovering binding sites within functional regions and remote allosteric locations. This thorough exploration ensures no potential mechanism of action is overlooked, aiming to discover novel therapeutic targets and lead compounds across an extensive 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.


PARTNER
Receptor.AI
 
UPACC
Q8NFM7

UPID:
I17RD_HUMAN

ALTERNATIVE NAMES:
IL17Rhom; Interleukin-17 receptor-like protein; Sef homolog

ALTERNATIVE UPACC:
Q8NFM7; Q2NKP7; Q58EZ7; Q6RVF4; Q6UWI5; Q8N113; Q8NFS0; Q9UFA0

BACKGROUND:
The Interleukin-17 receptor D, known alternatively as IL17Rhom and Sef homolog, is integral to several cellular processes. It inhibits the fibroblast growth factor mediated Ras-MAPK signaling and ERK activation, controls the spatial distribution of nuclear ERK signaling, and may be involved in JNK activation and apoptosis. IL17RD also plays a role in the early development of GnRH-secreting neurons and inhibits epithelial-to-mesenchymal transition in lens epithelial cells.

THERAPEUTIC SIGNIFICANCE:
Given IL17RD's role in Hypogonadotropic hypogonadism 18 with or without anosmia, exploring its functions further could lead to novel therapeutic approaches. This protein's involvement in critical signaling pathways and disease mechanisms underscores its potential as a target for drug discovery, aiming to address disorders linked to gonadotropin-releasing hormone deficiency and related symptoms.

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