Focused On-demand Library for Rho-associated protein kinase 1

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.

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

Fig. 1. The sreening workflow of Receptor.AI

This approach involves comprehensive molecular simulations of the catalytic and allosteric binding pockets and ensemble virtual screening that accounts for their conformational flexibility. In the case of designing modulators, the structural adjustments caused by reaction intermediates are considered to improve 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

Q13464

UPID:

ROCK1_HUMAN

Alternative names:

Renal carcinoma antigen NY-REN-35; Rho-associated, coiled-coil-containing protein kinase 1; Rho-associated, coiled-coil-containing protein kinase I; p160 ROCK-1

Alternative UPACC:

Q13464; B0YJ91; Q2KHM4; Q59GZ4

Background:

Rho-associated protein kinase 1 (ROCK-1), identified as Renal carcinoma antigen NY-REN-35, plays a pivotal role in actin cytoskeleton organization, cell polarity, and smooth muscle contraction. It is instrumental in various cellular processes including stress fiber formation, focal adhesion, and cell motility through phosphorylation of multiple targets such as DAPK3 and LIMK1. ROCK-1's involvement extends to non-apoptotic membrane blebbing, erythroid differentiation, and keratinocyte terminal differentiation.

Therapeutic significance:

Understanding the role of Rho-associated protein kinase 1 could open doors to potential therapeutic strategies.