AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Melanoma-associated antigen D2

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.

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 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 sreening workflow of Receptor.AI

Utilising molecular simulations, our approach thoroughly examines a wide array of proteins, tracking their conformational changes individually and within complexes. Ensemble virtual screening enables us to address conformational flexibility, revealing essential binding sites at functional regions and allosteric locations. Our rigorous analysis guarantees that no potential mechanism of action is overlooked, aiming to uncover new therapeutic targets and lead compounds across diverse biological functions.

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.

partner

Reaxense

upacc

Q9UNF1

UPID:

MAGD2_HUMAN

Alternative names:

11B6; Breast cancer-associated gene 1 protein; Hepatocellular carcinoma-associated protein JCL-1; MAGE-D2 antigen

Alternative UPACC:

Q9UNF1; A6NMX0; O76058; Q5BJF3; Q8NAL6; Q9H218; Q9P0U9; Q9UM52

Background:

Melanoma-associated antigen D2 (MAGE-D2), also known by alternative names such as 11B6, Breast cancer-associated gene 1 protein, Hepatocellular carcinoma-associated protein JCL-1, and MAGE-D2 antigen, plays a crucial role in the body's salt balance. It regulates the expression, localization to the plasma membrane, and function of sodium chloride cotransporters SLC12A1 and SLC12A3, essential for salt reabsorption in the distal renal tubule.

Therapeutic significance:

MAGE-D2's involvement in Bartter syndrome 5, an antenatal, transient form characterized by impaired salt reabsorption, highlights its potential as a target for therapeutic intervention. Understanding the role of MAGE-D2 could open doors to potential therapeutic strategies for managing this condition.

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