Focused On-demand Library for Ribonucleoside-diphosphate reductase subunit M2

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

The specialised, focused library is developed on demand with the most recent virtual screening and parameter assessment technology, guided by the Receptor.AI drug discovery platform. This approach exceeds the capabilities of traditional methods and offers compounds with higher 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 our associate Reaxense.

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 employ our advanced, specialised process to create targeted libraries for enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

The procedure entails thorough molecular simulations of the catalytic and allosteric binding pockets, accompanied by ensemble virtual screening that factors in their conformational flexibility. When developing modulators, the structural modifications brought about by reaction intermediates are factored in to optimize activity and selectivity.

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:

Ribonucleotide reductase small chain; Ribonucleotide reductase small subunit

Alternative UPACC:

P31350; B2R9B5; J3KP43; Q5WRU7


The Ribonucleoside-diphosphate reductase subunit M2, also known as Ribonucleotide reductase small chain or subunit, plays a crucial role in DNA synthesis. It catalyzes the conversion of ribonucleotides into deoxyribonucleotides, the building blocks necessary for DNA replication and repair. Additionally, it serves as an inhibitor of Wnt signaling, a pathway critical for cell development and growth.

Therapeutic significance:

Understanding the role of Ribonucleoside-diphosphate reductase subunit M2 could open doors to potential therapeutic strategies. Its pivotal function in DNA synthesis and cell signaling pathways makes it a promising target for cancer research and treatment, as well as for disorders related to abnormal cell growth and differentiation.

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.