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 carefully select specific compounds from a vast collection of over 60 billion molecules in virtual chemical space. Reaxense helps in synthesizing and delivering these compounds.


Contained in the library are leading modulators, each labelled with 38 ADME-Tox and 32 physicochemical and drug-likeness qualities. In addition, each compound is illustrated with its optimal docking poses, affinity scores, and activity scores, giving a complete picture.


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


 

Fig. 1. The screening workflow of Receptor.AI

By deploying molecular simulations, our approach comprehensively covers a broad array of proteins, tracking their flexibility and dynamics individually and within complexes. Ensemble virtual screening is utilised to take into account conformational dynamics, identifying pivotal binding sites located within functional regions and at allosteric locations. This thorough exploration ensures that every conceivable mechanism of action is considered, aiming to identify new therapeutic targets and advance lead compounds throughout a vast spectrum of biological functions.


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
Q9BUI4

UPID:
RPC3_HUMAN

ALTERNATIVE NAMES:
DNA-directed RNA polymerase III subunit C; RNA polymerase III 62 kDa subunit

ALTERNATIVE UPACC:
Q9BUI4; O15317; Q9Y3R6

BACKGROUND:
The protein DNA-directed RNA polymerase III subunit RPC3, alternatively named RNA polymerase III 62 kDa subunit, is integral to the transcription process, specifically in synthesizing small RNAs. It interacts with the TFIIIB-DNA complex, playing a role in the recruitment and stabilization of RNA polymerase III. Additionally, it acts as a DNA sensor in the innate immune system, detecting non-self dsDNA and triggering immune responses through the RIG-I pathway.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of DNA-directed RNA polymerase III subunit RPC3 offers a promising avenue for developing novel therapeutic approaches.

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