Focused On-demand Library for Mediator of RNA polymerase II transcription subunit 17

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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. Our collaborator, Reaxense, aids in their synthesis and provision.

The library includes a list of the most promising modulators annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Also, each compound is presented with its optimal docking poses, affinity scores, and activity scores, providing a comprehensive overview.

We utilise our cutting-edge, exclusive workflow to develop focused 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.

Our library distinguishes itself through several key aspects:

  • The Receptor.AI platform integrates all available data about the target protein, including past experiments, literature data, known ligands, structural information and more. This consolidated approach maximises the probability of prioritising highly relevant compounds.
  • The platform uses sophisticated molecular simulations to identify possible binding sites so that the compounds in the focused library are suitable for discovering allosteric inhibitors and the binders for cryptic pockets.
  • The platform integrates over 50 highly customisable AI models, which are thoroughly tested and validated on a multitude of commercial drug discovery programs and research projects. It is designed to be efficient, reliable and accurate. All this power is utilised when producing the focused libraries.
  • In addition to producing the focused libraries, Receptor.AI provides services and end-to-end solutions at every stage of preclinical drug discovery. The pricing model is success-based, which reduces your risks and leverages the mutual benefits of the project's success.







Alternative names:

Activator-recruited cofactor 77 kDa component; Cofactor required for Sp1 transcriptional activation subunit 6; Mediator complex subunit 17; Thyroid hormone receptor-associated protein complex 80 kDa component; Transcriptional coactivator CRSP77; Vitamin D3 receptor-interacting protein complex 80 kDa component

Alternative UPACC:

Q9NVC6; B3KN07; Q9HA81; Q9UNP7; Q9Y2W0; Q9Y660


Mediator of RNA polymerase II transcription subunit 17 plays a pivotal role in bridging gene-specific regulatory proteins with the basal RNA polymerase II transcription machinery. This ensures the regulated transcription of nearly all RNA polymerase II-dependent genes. Known by various names, including Mediator complex subunit 17 and Activator-recruited cofactor 77 kDa component, it is essential for conveying genetic information.

Therapeutic significance:

Linked to Microcephaly, postnatal progressive, with seizures and brain atrophy, understanding the role of Mediator of RNA polymerase II transcription subunit 17 could open doors to potential therapeutic strategies. Its involvement in severe developmental disorders underscores the importance of targeted research in uncovering treatment options.

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