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.


From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Reaxense aids in their synthesis and provision.


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.


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


 

Fig. 1. The screening 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 stands out due to several important features:


  • The Receptor.AI platform compiles comprehensive data on the target protein, encompassing previous experiments, literature, known ligands, structural details, and more, leading to a higher chance of selecting the most relevant compounds.

  • Advanced molecular simulations on the platform help pinpoint potential binding sites, making the compounds in our focused library ideal for finding allosteric inhibitors and targeting cryptic pockets.

  • Receptor.AI boasts over 50 tailor-made AI models, rigorously tested and proven in various drug discovery projects and research initiatives. They are crafted for efficacy, dependability, and precision, all of which are key in creating our focused libraries.

  • Beyond creating focused libraries, Receptor.AI offers comprehensive services and complete solutions throughout the preclinical drug discovery phase. Our success-based pricing model minimises risk and maximises the mutual benefits of the project's success.


PARTNER
Receptor.AI
 
UPACC
P53803

UPID:
RPAB4_HUMAN

ALTERNATIVE NAMES:
ABC10-alpha; DNA-directed RNA polymerase II subunit K; RNA polymerase II 7.0 kDa subunit; RPB10alpha

ALTERNATIVE UPACC:
P53803; Q6IBD4

BACKGROUND:
The protein DNA-directed RNA polymerases I, II, and III subunit RPABC4, also referred to as DNA-directed RNA polymerase II subunit K and RNA polymerase II 7.0 kDa subunit, is crucial for the transcription of DNA into RNA. It uses four ribonucleoside triphosphates as substrates and is a key component of RNA polymerases I, II, and III. These enzymes are responsible for the synthesis of ribosomal RNA precursors, mRNA precursors, various functional non-coding RNAs, and small RNAs, including 5S rRNA and tRNAs.

THERAPEUTIC SIGNIFICANCE:
Understanding the role of DNA-directed RNA polymerases I, II, and III subunit RPABC4 could open doors to potential therapeutic strategies.

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