Focused On-demand Library for Ribosomal RNA-processing protein 7 homolog A

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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.

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

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.

 Fig. 1. The sreening workflow of Receptor.AI

Our strategy employs molecular simulations to explore an extensive range of proteins, capturing their dynamics both individually and within complexes with other proteins. Through ensemble virtual screening, we address proteins' conformational mobility, uncovering key binding sites at both functional regions and remote allosteric locations. This comprehensive investigation ensures a thorough assessment of all potential mechanisms of action, with the goal of discovering innovative therapeutic targets and lead molecules across across diverse 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.







Alternative names:

Gastric cancer antigen Zg14

Alternative UPACC:

Q9Y3A4; A4FTX2; B2RBG4; Q0VAD0; Q5JZ94; Q6P4B5; Q8IVR9; Q8IVY0; Q8N5Q3; Q8NEY6; Q9Y3H5


Ribosomal RNA-processing protein 7 homolog A, also known as Gastric cancer antigen Zg14, plays a crucial role in ribosomal RNA processing and is pivotal in neurogenesis and neocortex development. It is part of the small subunit processome, facilitating RNA folding, modifications, and cleavage, essential for ribosome biogenesis.

Therapeutic significance:

Linked to Microcephaly 28, primary, autosomal recessive, characterized by significantly reduced head size and impaired intellectual development, understanding the role of Ribosomal RNA-processing protein 7 homolog A could open doors to potential therapeutic strategies.

Looking for more information on this library or underlying technology? Fill out the form below and we'll be in touch with all the details you need.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.