AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Cell adhesion molecule-related/down-regulated by oncogenes

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.

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 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 employ our advanced, specialised process to create targeted libraries.

 Fig. 1. The sreening workflow of Receptor.AI

Our methodology employs molecular simulations to explore a wide array of proteins, capturing their dynamic states both individually and within complexes. Through ensemble virtual screening, we address conformational mobility, uncovering binding sites within functional regions and remote allosteric locations. This thorough exploration ensures no potential mechanism of action is overlooked, aiming to discover novel therapeutic targets and lead compounds across an extensive spectrum of 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

Q4KMG0

UPID:

CDON_HUMAN

Alternative names:

-

Alternative UPACC:

Q4KMG0; O14631

Background:

The Cell adhesion molecule-related/down-regulated by oncogenes (CAM-related/DRO) protein, encoded by the gene with accession number Q4KMG0, plays a pivotal role in cell-cell interactions essential for the differentiation of myogenic cells. This protein is a key component of a cell-surface receptor complex that orchestrates the precise communication between muscle precursor cells, facilitating their proper differentiation.

Therapeutic significance:

Given its involvement in Holoprosencephaly 11, a brain development disorder resulting from the failure of the forebrain to correctly separate into hemispheres, understanding the role of CAM-related/DRO could open doors to potential therapeutic strategies. Its genetic association with this condition underscores its potential as a target for therapeutic intervention.

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