AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Endogenous retrovirus group K member 9 Pol protein

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense 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 carefully select specific compounds from a vast collection of over 60 billion molecules in virtual chemical space. Our partner 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 employ our advanced, specialised process to create targeted libraries.

 Fig. 1. The sreening 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

Reaxense

upacc

P63128

UPID:

POK9_HUMAN

Alternative names:

HERV-K(C6) Gag-Pol protein; HERV-K109 Gag-Pol protein; HERV-K_6q14.1 provirus ancestral Gag-Pol polyprotein

Alternative UPACC:

P63128; Q9UKH4

Background:

The Endogenous retrovirus group K member 9 Pol protein, known by alternative names such as HERV-K(C6) Gag-Pol protein and HERV-K109 Gag-Pol protein, plays a pivotal role in the viral replication cycle. It is involved in assembly, budding, maturation, and infection stages, facilitating membrane associations, self-associations, and packaging of genomic RNA. Its reverse transcriptase and integrase functions are crucial for converting viral RNA into DNA and integrating it into the host cell chromosome.

Therapeutic significance:

Understanding the role of Endogenous retrovirus group K member 9 Pol protein could open doors to potential therapeutic strategies.

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