Focused On-demand Libraries - Receptor.AI 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.


Our selection of compounds is from a large virtual library of over 60 billion molecules. The production and distribution of these compounds are managed by Reaxense.


In the library, a selection of top modulators is provided, each marked with 38 ADME-Tox and 32 parameters related to physicochemical properties and drug-likeness. Also, every compound comes with its best 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 screening workflow of Receptor.AI

By deploying molecular simulations, our approach comprehensively covers a broad array of proteins, tracking their flexibility and dynamics individually and within complexes. Ensemble virtual screening is utilised to take into account conformational dynamics, identifying pivotal binding sites located within functional regions and at allosteric locations. This thorough exploration ensures that every conceivable mechanism of action is considered, aiming to identify new therapeutic targets and advance lead compounds throughout a vast spectrum of 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.


PARTNER
Receptor.AI
 
UPACC
Q86SQ7

UPID:
SDCG8_HUMAN

ALTERNATIVE NAMES:
Antigen NY-CO-8; Centrosomal colon cancer autoantigen protein

ALTERNATIVE UPACC:
Q86SQ7; O60527; Q3ZCR6; Q8N5F2; Q9P0F1

BACKGROUND:
The protein known as Serologically defined colon cancer antigen 8, with alternative names Antigen NY-CO-8 and Centrosomal colon cancer autoantigen protein, is integral to establishing cell polarity, epithelial lumen formation, and initiating ciliogenesis. It achieves this through its interaction with RABEP2, facilitating centrosomal localization critical for primary cilia formation and subsequent Hedgehog signaling.

THERAPEUTIC SIGNIFICANCE:
Understanding the role of Serologically defined colon cancer antigen 8 could open doors to potential therapeutic strategies for addressing complex diseases such as Senior-Loken syndrome 7 and Bardet-Biedl syndrome 16, by targeting its essential functions in ciliogenesis and Hedgehog signaling.

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