AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Endogenous retrovirus group K member 104 Pro 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.

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

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 sreening workflow of Receptor.AI

Our strategy employs molecular simulations to explore an extensive range of proteins, capturing their dynamics both individually and within complexes with other proteins. Through ensemble virtual screening, we address proteins' conformational mobility, uncovering key binding sites at both functional regions and remote allosteric locations. This comprehensive investigation ensures a thorough assessment of all potential mechanisms of action, with the goal of discovering innovative therapeutic targets and lead molecules across across diverse 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

Reaxense

upacc

P63124

UPID:

VPK04_HUMAN

Alternative names:

HERV-K104 Pro protein; HERV-K_5q13.3 provirus ancestral Pro protein; Protease; Proteinase

Alternative UPACC:

P63124

Background:

The Endogenous retrovirus group K member 104 Pro protein, known by alternative names such as HERV-K104 Pro protein and Protease, plays a crucial role in the life cycle of retroviruses. It is involved in the processing of primary translation products and the maturation of viral particles. This protein's evolutionary journey suggests it may have retained, lost, or modified its original function, highlighting its adaptability and significance in viral evolution.

Therapeutic significance:

Understanding the role of Endogenous retrovirus group K member 104 Pro protein could open doors to potential therapeutic strategies. Its involvement in the maturation of viral particles makes it a target for antiviral drug development, offering a promising avenue for the treatment of retrovirus-related diseases.

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