AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Cancer-related nucleoside-triphosphatase

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

This comprehensive focused library is produced on demand with state-of-the-art virtual screening and parameter assessment technology driven by Receptor.AI drug discovery platform. This approach outperforms traditional methods and provides higher-quality compounds with superior 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.

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 employ our advanced, specialised process to create targeted libraries for enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

The method includes detailed molecular simulations of the catalytic and allosteric binding pockets, along with ensemble virtual screening that considers their conformational flexibility. In the design of modulators, structural changes induced by reaction intermediates are taken into account to enhance activity and selectivity.

Our library distinguishes itself through several key aspects:

  • The Receptor.AI platform integrates all available data about the target protein, including past experiments, literature data, known ligands, structural information and more. This consolidated approach maximises the probability of prioritising highly relevant compounds.
  • The platform uses sophisticated molecular simulations to identify possible binding sites so that the compounds in the focused library are suitable for discovering allosteric inhibitors and the binders for cryptic pockets.
  • The platform integrates over 50 highly customisable AI models, which are thoroughly tested and validated on a multitude of commercial drug discovery programs and research projects. It is designed to be efficient, reliable and accurate. All this power is utilised when producing the focused libraries.
  • In addition to producing the focused libraries, Receptor.AI provides services and end-to-end solutions at every stage of preclinical drug discovery. The pricing model is success-based, which reduces your risks and leverages the mutual benefits of the project's success.

partner

Reaxense

upacc

Q9BSD7

UPID:

NTPCR_HUMAN

Alternative names:

Nucleoside triphosphate phosphohydrolase

Alternative UPACC:

Q9BSD7

Background:

The Cancer-related nucleoside-triphosphatase, also known as Nucleoside triphosphate phosphohydrolase, exhibits a crucial enzymatic activity by hydrolyzing ATP, GTP, CTP, TTP, and UTP. Its ability to also hydrolyze nucleoside diphosphates, albeit with lower efficiency, underscores its versatile role in cellular metabolism.

Therapeutic significance:

Understanding the role of Cancer-related nucleoside-triphosphatase could open doors to potential therapeutic strategies. Its enzymatic functions suggest a pivotal role in nucleotide metabolism, which is essential for cell growth and proliferation, making it a potential target for cancer therapy.

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