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.


From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Reaxense aids in their synthesis and provision.


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 employ our advanced, specialised process to create targeted 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
Q99832

UPID:
TCPH_HUMAN

ALTERNATIVE NAMES:
CCT-eta; HIV-1 Nef-interacting protein

ALTERNATIVE UPACC:
Q99832; A8K7E6; A8MWI8; B7WNW9; B7Z4T9; B7Z4Z7; O14871; Q6FI26

BACKGROUND:
The T-complex protein 1 subunit eta, known alternatively as CCT-eta and HIV-1 Nef-interacting protein, is a key player in the chaperonin-containing T-complex (TRiC). This complex is vital for the proper folding of proteins following ATP hydrolysis. It has a pivotal role in mediating the folding of WRAP53/TCAB1, crucial for telomere maintenance, and is probable in the folding of actin and tubulin.

THERAPEUTIC SIGNIFICANCE:
Exploring the function of T-complex protein 1 subunit eta unveils potential avenues for therapeutic intervention. Given its critical role in protein folding and maintenance of telomere integrity, targeting this protein could offer new therapeutic approaches for conditions associated with protein misfolding or telomere-related abnormalities.

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