Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

This extensive focused library is tailor-made using the latest virtual screening and parameter assessment technology, operated by the Receptor.AI drug discovery platform. This technique is more effective than traditional methods, offering compounds with improved activity, selectivity, and safety.


We carefully select specific compounds from a vast collection of over 60 billion molecules in virtual chemical space. Reaxense helps in synthesizing and delivering these compounds.


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 use our state-of-the-art dedicated workflow for designing 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.


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
Q5T4F4

UPID:
ZFY27_HUMAN

ALTERNATIVE NAMES:
Spastic paraplegia 33 protein; Zinc finger FYVE domain-containing protein 27

ALTERNATIVE UPACC:
Q5T4F4; B7Z3S0; B7Z404; B7Z626; G8JLC3; G8JLF0; J3KP98; Q5T4F1; Q5T4F2; Q5T4F3; Q8N1K0; Q8N6D6; Q8NCA0; Q8NDE4; Q96M08

BACKGROUND:
Protrudin serves as a key regulator in neurite extension, facilitating axonal growth and neuronal polarity through its interaction with proteins like KIF5A, VAPA, and RAB11. It also plays a role in the formation and stabilization of the tubular ER network, essential for proper cellular function.

THERAPEUTIC SIGNIFICANCE:
Given its role in Spastic paraplegia 33, autosomal dominant, Protrudin represents a promising therapeutic target. Exploring the mechanisms by which Protrudin influences neurodegeneration could unlock new avenues for the treatment of spastic paraplegia and enhance our understanding of neuronal disorders.

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