AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Inactive heparanase-2

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

The focused library is created on demand with the latest virtual screening and parameter assessment technology, supported by the Receptor.AI drug discovery platform. This method is more effective than traditional methods and results in higher-quality compounds with better activity, selectivity, and safety.

The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is facilitated by our partner Reaxense.

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.

 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.

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

Q8WWQ2

UPID:

HPSE2_HUMAN

Alternative names:

-

Alternative UPACC:

Q8WWQ2; Q5VUH4; Q5VUH5; Q5VUH6; Q8WWQ1; Q9HB37; Q9HB38; Q9HB39

Background:

Inactive heparanase-2, encoded by the gene with accession number Q8WWQ2, is a protein that binds heparin and heparan sulfate with high affinity. Despite its name, it lacks heparanase activity and instead plays a role in inhibiting HPSE by competing for its substrates. This unique function distinguishes it from other proteins within its family.

Therapeutic significance:

The protein's involvement in Urofacial syndrome 1, a rare autosomal recessive disorder, underscores its clinical importance. Understanding the role of Inactive heparanase-2 could open doors to potential therapeutic strategies for this condition, which leads to significant kidney damage and renal failure due to urinary complications.

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