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.


The library includes a list of the most promising modulators annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Also, each compound is presented with its optimal docking poses, affinity scores, and activity scores, providing a comprehensive overview.


Our top-notch dedicated system is used to design specialised 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
Q96EV8

UPID:
DTBP1_HUMAN

ALTERNATIVE NAMES:
Biogenesis of lysosome-related organelles complex 1 subunit 8; Dysbindin-1; Dystrobrevin-binding protein 1; Hermansky-Pudlak syndrome 7 protein

ALTERNATIVE UPACC:
Q96EV8; A8K3V3; Q5THY3; Q5THY4; Q96NV2; Q9H0U2; Q9H3J5

BACKGROUND:
Dysbindin, or Dystrobrevin-binding protein 1, is integral to the normal biogenesis of lysosome-related organelles and plays a role in the regulation of cell surface exposure of DRD2. It influences neuronal transmission and viability through modulating PI3-kinase-Akt signaling and glutamatergic release.

THERAPEUTIC SIGNIFICANCE:
Given its involvement in Hermansky-Pudlak syndrome 7 and its role in neurotransmitter release and synaptic vesicle trafficking, Dysbindin presents a promising target for developing treatments for related neurological and lysosomal storage disorders. Understanding the role of Dysbindin could open doors to potential therapeutic strategies.

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