Focused On-demand Libraries - Receptor.AI 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.


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.


Key features that set our library apart include:


  • The Receptor.AI platform integrates extensive information about the target protein, such as historical experiments, academic research, known ligands, and structural insights, thereby increasing the likelihood of identifying highly relevant compounds.

  • The platform’s sophisticated molecular simulations are designed to discover potential binding sites, ensuring that our focused library is optimal for the discovery of allosteric inhibitors and binders for cryptic pockets.

  • With over 50 customisable AI models, verified through extensive testing in commercial drug discovery and research, Receptor.AI is efficient, reliable, and precise. These models are essential in the production of our focused libraries.

  • Receptor.AI not only produces focused libraries but also provides full services and solutions at every stage of preclinical drug discovery, with a success-based pricing structure that aligns our interests with the success of your project.


PARTNER
Receptor.AI
 
UPACC
Q15717

UPID:
ELAV1_HUMAN

ALTERNATIVE NAMES:
Hu-antigen R

ALTERNATIVE UPACC:
Q15717; B4DVB8; Q53XN6; Q9BTT1

BACKGROUND:
Hu-antigen R, or ELAV-like protein 1, is identified for its RNA-binding capabilities, particularly to the 3'-UTR regions of mRNAs, thereby increasing their stability. It is instrumental in embryonic stem cell differentiation, favoring mRNAs without N6-methyladenosine (m6A) methylation. The protein's ability to bind m6A-containing mRNAs, such as those of MYC, and its interaction with AU-rich elements in the 3'-UTR of target mRNAs, underscores its regulatory role in gene expression, impacting proteins like FOS and IL3.

THERAPEUTIC SIGNIFICANCE:
Understanding the role of ELAV-like protein 1 could open doors to potential therapeutic strategies.

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