Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

The focused library is created on demand with the latest virtual screening and parameter assessment technology, supported by the Receptor.AI drug discovery platform. This method is more effective than traditional methods and results in higher-quality compounds with better 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.


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.


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.


Key features that set our library apart include:


  • The Receptor.AI platform integrates extensive information about the target protein, such as historical experiments, academic research, known ligands, and structural insights, thereby increasing the likelihood of identifying highly relevant compounds.

  • The platform’s sophisticated molecular simulations are designed to discover potential binding sites, ensuring that our focused library is optimal for the discovery of allosteric inhibitors and binders for cryptic pockets.

  • With over 50 customisable AI models, verified through extensive testing in commercial drug discovery and research, Receptor.AI is efficient, reliable, and precise. These models are essential in the production of our focused libraries.

  • Receptor.AI not only produces focused libraries but also provides full services and solutions at every stage of preclinical drug discovery, with a success-based pricing structure that aligns our interests with the success of your project.


PARTNER
Receptor.AI
 
UPACC
P63121

UPID:
VP113_HUMAN

ALTERNATIVE NAMES:
HERV-K113 envelope protein; HERV-K_19p13.11 provirus ancestral Pro protein; Protease; Proteinase

ALTERNATIVE UPACC:
P63121

BACKGROUND:
Retroviral proteases, including the Endogenous retrovirus group K member 113 Pro protein, are essential for the processing and maturation of viral particles. This protein, also known as Protease or Proteinase, has evolved to play significant roles in the life cycle of endogenous retroviruses, highlighting its importance in viral evolution and pathogenesis.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of the Endogenous retrovirus group K member 113 Pro protein could lead to groundbreaking therapeutic strategies. Its critical role in the retroviral life cycle presents an opportunity for the development of novel antiviral therapies, potentially transforming the treatment landscape for retrovirus-related conditions.

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