Focused On-demand Library for ATP-binding cassette sub-family C member 6

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.

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 high-tech, dedicated method is applied to construct targeted libraries for enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

It includes comprehensive molecular simulations of the catalytic and allosteric binding pockets and the ensemble virtual screening accounting for their conformational mobility. In the case of designing modulators, the structural changes induced by reaction intermediates are taken into account to leverage activity and selectivity.

Our library is unique due to several crucial aspects:

  • Receptor.AI compiles all relevant data on the target protein, such as past experimental results, literature findings, known ligands, and structural data, thereby enhancing the likelihood of focusing on the most significant compounds.
  • By utilizing advanced molecular simulations, the platform is adept at locating potential binding sites, rendering the compounds in the focused library well-suited for unearthing allosteric inhibitors and binders for hidden pockets.
  • The platform is supported by more than 50 highly specialized AI models, all of which have been rigorously tested and validated in diverse drug discovery and research programs. Its design emphasizes efficiency, reliability, and accuracy, crucial for producing focused libraries.
  • Receptor.AI extends beyond just creating focused libraries; it offers a complete spectrum of services and solutions during the preclinical drug discovery phase, with a success-dependent pricing strategy that reduces risk and fosters shared success in the project.







Alternative names:

Anthracycline resistance-associated protein; Multi-specific organic anion transporter E; Multidrug resistance-associated protein 6

Alternative UPACC:

O95255; A2RRN8; A8KIG6; A8Y988; E7ESW8; P78420; Q8TCY8; Q9UMZ7


ATP-binding cassette sub-family C member 6 (ABCC6) plays a pivotal role in cellular detoxification and transport processes. Known by alternative names such as Anthracycline resistance-associated protein and Multi-specific organic anion transporter E, ABCC6 is an ATP-dependent transporter that actively extrudes a variety of physiological compounds and xenobiotics from cells. It is involved in the regulation of organic compound transport across the blood-testis-barrier and plays a crucial role in inorganic pyrophosphate (PPi) homeostasis by mediating the release of nucleoside triphosphates into the circulation.

Therapeutic significance:

ABCC6's dysfunction is linked to Pseudoxanthoma elasticum, a multisystem disorder characterized by mineralized elastic fibers in various tissues, and Generalized arterial calcification of infancy, 2, marked by early-onset arterial calcification. Understanding the role of ABCC6 could open doors to potential therapeutic strategies for these conditions, highlighting its significance in drug discovery for cardiovascular and connective tissue disorders.

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.