Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

Our detailed focused library is generated on demand with advanced virtual screening and parameter assessment technology powered by the Receptor.AI drug discovery platform. This method surpasses traditional approaches, delivering compounds of better quality with enhanced 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.


The library includes a list of the most effective modulators, each annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Furthermore, each compound is shown with its optimal docking poses, affinity scores, and activity scores, offering a detailed summary.


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


 

Fig. 1. The screening workflow of Receptor.AI

Our strategy employs molecular simulations to explore an extensive range of proteins, capturing their dynamics both individually and within complexes with other proteins. Through ensemble virtual screening, we address proteins' conformational mobility, uncovering key binding sites at both functional regions and remote allosteric locations. This comprehensive investigation ensures a thorough assessment of all potential mechanisms of action, with the goal of discovering innovative therapeutic targets and lead molecules across across diverse biological functions.


Several key aspects differentiate our library:


  • Receptor.AI compiles an all-encompassing dataset on the target protein, including historical experiments, literature data, known ligands, and structural insights, maximising the chances of prioritising the most pertinent compounds.

  • The platform employs state-of-the-art molecular simulations to identify potential binding sites, ensuring the focused library is primed for discovering allosteric inhibitors and binders of concealed pockets.

  • Over 50 customisable AI models, thoroughly evaluated in various drug discovery endeavours and research projects, make Receptor.AI both efficient and accurate. This technology is integral to the development of our focused libraries.

  • In addition to generating focused libraries, Receptor.AI offers a full range of services and solutions for every step of preclinical drug discovery, with a pricing model based on success, thereby reducing risk and promoting joint project success.


PARTNER
Receptor.AI
 
UPACC
P55145

UPID:
MANF_HUMAN

ALTERNATIVE NAMES:
Arginine-rich protein; Protein ARMET

ALTERNATIVE UPACC:
P55145; Q14CX4; Q86U67; Q96IS4

BACKGROUND:
The Mesencephalic astrocyte-derived neurotrophic factor, known as Protein ARMET, selectively supports ventral mid-brain dopaminergic neurons. It influences GABAergic transmission to substantia nigra dopaminergic neurons, increasing GABAergic inhibitory postsynaptic currents. ARMET also prevents cell proliferation and ER stress-induced apoptosis. Under normal conditions, it associates with the ER chaperone HSPA5, but under ER stress and hypoxia, it is up-regulated and secreted, binding to 3-O-sulfogalactosylceramide on target cells to mitigate ER stress and cell toxicity.

THERAPEUTIC SIGNIFICANCE:
Understanding the role of Mesencephalic astrocyte-derived neurotrophic factor could open doors to potential therapeutic strategies.

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