Focused On-demand Library for Cell cycle checkpoint protein RAD17

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.

Our selection of compounds is from a large virtual library of over 60 billion molecules. The production and distribution of these compounds are managed by our partner Reaxense.

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

By deploying molecular simulations, our approach comprehensively covers a broad array of proteins, tracking their flexibility and dynamics individually and within complexes. Ensemble virtual screening is utilised to take into account conformational dynamics, identifying pivotal binding sites located within functional regions and at allosteric locations. This thorough exploration ensures that every conceivable mechanism of action is considered, aiming to identify new therapeutic targets and advance lead compounds throughout a vast 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

O75943

UPID:

RAD17_HUMAN

Alternative names:

RF-C/activator 1 homolog

Alternative UPACC:

O75943; A8K8X2; D3DWA5; O75714; Q7Z3S4; Q9UNK7; Q9UNR7; Q9UNR8; Q9UPF5

Background:

Cell cycle checkpoint protein RAD17, also known as RF-C/activator 1 homolog, plays a pivotal role in cell growth, chromosomal stability, and DNA damage response. It exhibits a weak ATPase activity essential for chromatin binding, facilitating the recruitment of RAD1-RAD9-HUS1 complex and RHNO1, crucial for CHEK1 activation. RAD17 may also act as a DNA replication progress sensor and participate in homologous recombination.

Therapeutic significance:

Understanding the role of Cell cycle checkpoint protein RAD17 could open doors to potential therapeutic strategies.