Focused On-demand Library for Antiviral innate immune response receptor RIG-I

Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

The focused library is created on demand with the latest virtual screening and parameter assessment technology, supported by the Receptor.AI drug discovery platform. This method is more effective than traditional methods and results in higher-quality compounds with better 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 effective modulators, each annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Furthermore, each compound is shown with its optimal docking poses, affinity scores, and activity scores, offering a detailed summary.


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


 

Fig. 1. The screening workflow of Receptor.AI

This process includes extensive molecular simulations of the receptor in its native membrane environment, along with ensemble virtual screening that accounts for its conformational mobility. In the case of dimeric or oligomeric receptors, the entire functional complex is modelled, identifying potential binding pockets on and between the subunits to encompass all possible mechanisms of action.


Several key aspects differentiate our library:


  • Receptor.AI compiles an all-encompassing dataset on the target protein, including historical experiments, literature data, known ligands, and structural insights, maximising the chances of prioritising the most pertinent compounds.

  • The platform employs state-of-the-art molecular simulations to identify potential binding sites, ensuring the focused library is primed for discovering allosteric inhibitors and binders of concealed pockets.

  • Over 50 customisable AI models, thoroughly evaluated in various drug discovery endeavours and research projects, make Receptor.AI both efficient and accurate. This technology is integral to the development of our focused libraries.

  • In addition to generating focused libraries, Receptor.AI offers a full range of services and solutions for every step of preclinical drug discovery, with a pricing model based on success, thereby reducing risk and promoting joint project success.

PARTNER
Receptor.AI
 
UPACC
O95786

UPID:
RIGI_HUMAN

ALTERNATIVE NAMES:
ATP-dependent RNA helicase DDX58; DEAD box protein 58; RIG-I-like receptor 1; RNA sensor RIG-I; Retinoic acid-inducible gene 1 protein; Retinoic acid-inducible gene I protein

ALTERNATIVE UPACC:
O95786; A2RU81; Q5HYE1; Q5VYT1; Q9NT04

BACKGROUND:
Retinoic acid-inducible gene 1 protein (RIG-I) serves as a key sensor for cytoplasmic viral nucleic acids, initiating antiviral signaling pathways. Through its interaction with various viral RNAs, RIG-I activates downstream effectors that lead to the production of essential antiviral molecules. Its detection of a wide range of viruses, including members of the Paramyxoviridae and Flaviviridae families, positions RIG-I as a central figure in the immune response to viral infections.

THERAPEUTIC SIGNIFICANCE:
Given RIG-I's critical role in mediating the immune response to viral infections, its study offers promising avenues for the development of novel antiviral therapies. The protein's association with Singleton-Merten syndrome 2 further underscores its therapeutic relevance, suggesting that targeting RIG-I could yield innovative treatments for this and potentially other immune-related conditions.

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