Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

This comprehensive focused library is produced on demand with state-of-the-art virtual screening and parameter assessment technology driven by Receptor.AI drug discovery platform. This approach outperforms traditional methods and provides higher-quality compounds with superior activity, selectivity and safety.


From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Reaxense aids in their synthesis and provision.


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.


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


 

Fig. 1. The screening 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 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
Q9BUD6

UPID:
SPON2_HUMAN

ALTERNATIVE NAMES:
Differentially expressed in cancerous and non-cancerous lung cells 1; Mindin

ALTERNATIVE UPACC:
Q9BUD6; D3DVN9; Q4W5N4; Q9ULW1

BACKGROUND:
Spondin-2, recognized by its alternative names Mindin and differentially expressed in cancerous and non-cancerous lung cells 1, is integral to cell adhesion and neuronal development. It facilitates the adhesion and outgrowth of hippocampal embryonic neurons. Spondin-2's ability to bind directly to bacteria and their components, serving as an opsonin for macrophage phagocytosis, underscores its critical function in the innate immune response. It is distinguished as a unique pattern-recognition molecule in the extracellular matrix, targeting microbial pathogens by binding bacterial lipopolysaccharide (LPS).

THERAPEUTIC SIGNIFICANCE:
Understanding the role of Spondin-2 could open doors to potential therapeutic strategies.

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