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 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 utilise our cutting-edge, exclusive workflow to develop 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
Q9BXR3

UPID:
POK6_HUMAN

ALTERNATIVE NAMES:
HERV-K(C7) Pol protein; HERV-K(HML-2.HOM) Pol protein; HERV-K108 Pol protein; HERV-K_7p22.1 provirus ancestral Pol protein

ALTERNATIVE UPACC:
Q9BXR3; Q6KH04; Q9BXR4; Q9UKH5; Q9UP31; Q9WIK9; Q9WJR4

BACKGROUND:
Endogenous retrovirus group K member 6 Pol protein, known alternatively as HERV-K(HML-2.HOM) Pol protein and HERV-K_7p22.1 provirus ancestral Pol protein, is pivotal in viral DNA synthesis and integration. Its RNase H domain is essential for degrading RNA templates and excising RNA primers, facilitating the conversion of viral RNA to DNA and its subsequent integration into the host genome.

THERAPEUTIC SIGNIFICANCE:
Exploring the functions of Endogenous retrovirus group K member 6 Pol protein offers a pathway to novel therapeutic avenues. Its critical role in viral replication and integration highlights its potential as a target for innovative antiviral therapies.

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