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.


We pick out particular compounds from an extensive virtual database of more than 60 billion molecules. The preparation and shipment of these compounds are facilitated by Reaxense.


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.


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


 

Fig. 1. The screening workflow of Receptor.AI

Our methodology leverages molecular simulations to examine a vast array of proteins, capturing their dynamics in both isolated forms and in complexes with other proteins. Through ensemble virtual screening, we thoroughly account for the protein's conformational mobility, identifying critical binding sites within functional regions and distant allosteric locations. This detailed exploration ensures that we comprehensively assess every possible mechanism of action, with the objective of identifying novel therapeutic targets and lead compounds that span a wide spectrum of biological functions.


Our library stands out due to several important features:


  • The Receptor.AI platform compiles comprehensive data on the target protein, encompassing previous experiments, literature, known ligands, structural details, and more, leading to a higher chance of selecting the most relevant compounds.

  • Advanced molecular simulations on the platform help pinpoint potential binding sites, making the compounds in our focused library ideal for finding allosteric inhibitors and targeting cryptic pockets.

  • Receptor.AI boasts over 50 tailor-made AI models, rigorously tested and proven in various drug discovery projects and research initiatives. They are crafted for efficacy, dependability, and precision, all of which are key in creating our focused libraries.

  • Beyond creating focused libraries, Receptor.AI offers comprehensive services and complete solutions throughout the preclinical drug discovery phase. Our success-based pricing model minimises risk and maximises the mutual benefits of the project's success.


PARTNER
Receptor.AI
 
UPACC
Q9P0L0

UPID:
VAPA_HUMAN

ALTERNATIVE NAMES:
33 kDa VAMP-associated protein

ALTERNATIVE UPACC:
Q9P0L0; A6NDZ0; D3DUI3; O75453; Q5U0E7; Q9UBZ2

BACKGROUND:
The 33 kDa Vesicle-associated membrane protein-associated protein A (VAP-A) mediates critical cellular processes, including the formation of ER-late endosome contact sites and the recruitment of VAPA to the plasma membrane. Through its interaction with various proteins such as STARD3 and OSBPL3, VAP-A influences RRAS signaling pathways and integrin beta-1 activation, playing a significant role in cell surface dynamics. Its involvement in ER morphology and vesicle trafficking underscores its importance in cellular homeostasis.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of Vesicle-associated membrane protein-associated protein A offers a promising avenue for the development of novel therapeutic approaches.

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