AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Serologically defined colon cancer antigen 8

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

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.

partner

Reaxense

upacc

Q86SQ7

UPID:

SDCG8_HUMAN

Alternative names:

Antigen NY-CO-8; Centrosomal colon cancer autoantigen protein

Alternative UPACC:

Q86SQ7; O60527; Q3ZCR6; Q8N5F2; Q9P0F1

Background:

Serologically defined colon cancer antigen 8, also known as Antigen NY-CO-8, plays a pivotal role in cell polarity, epithelial lumen formation, and ciliogenesis. Its interaction with RABEP2 is crucial for centrosomal localization, essential for the formation of primary cilia and activation of the Hedgehog signaling pathway.

Therapeutic significance:

The protein's involvement in Senior-Loken syndrome 7 and Bardet-Biedl syndrome 16, through its role in ciliogenesis and Hedgehog signaling, highlights its potential as a target for therapeutic intervention in renal-retinal disorders and syndromes with complex phenotypes.

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