AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Wings apart-like protein homolog

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 promising modulators annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Also, each compound is presented with its optimal docking poses, affinity scores, and activity scores, providing a comprehensive overview.

We use our state-of-the-art dedicated workflow for designing focused 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 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

Q7Z5K2

UPID:

WAPL_HUMAN

Alternative names:

Friend of EBNA2 protein; WAPL cohesin release factor

Alternative UPACC:

Q7Z5K2; A7E2B5; Q5VSK5; Q8IX10; Q92549

Background:

The Wings apart-like protein homolog, also known as Friend of EBNA2 protein and WAPL cohesin release factor, plays a pivotal role in cell division. It is a key regulator of sister chromatid cohesion in mitosis, ensuring accurate chromosome partitioning by negatively regulating cohesin association with chromatin. This protein is crucial during both interphase for sister chromatid cohesion and the early stages of mitosis for sister-chromatid resolution. Its function of coupling DNA replication to sister chromatid cohesion underscores its importance in both meiotic and mitotic cells, as well as in DNA repair processes.

Therapeutic significance:

Understanding the role of Wings apart-like protein homolog could open doors to potential therapeutic strategies.

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