Focused On-demand Library for Probable ATP-dependent RNA helicase DDX53

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.

We pick out particular compounds from an extensive virtual database of more than 60 billion molecules. The preparation and shipment of these compounds are facilitated by our associate Reaxense.

Contained in the library are leading modulators, each labelled with 38 ADME-Tox and 32 physicochemical and drug-likeness qualities. In addition, each compound is illustrated with its optimal docking poses, affinity scores, and activity scores, giving a complete picture.

Our high-tech, dedicated method is applied to construct targeted libraries for enzymes.

Fig. 1. The sreening workflow of Receptor.AI

The procedure entails thorough molecular simulations of the catalytic and allosteric binding pockets, accompanied by ensemble virtual screening that factors in their conformational flexibility. When developing modulators, the structural modifications brought about by reaction intermediates are factored in to optimize 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

Q86TM3

UPID:

DDX53_HUMAN

Alternative names:

Cancer-associated gene protein; Cancer/testis antigen 26; DEAD box protein 53; DEAD box protein CAGE

Alternative UPACC:

Q86TM3; Q0D2N2; Q6NVV4

Background:

The Probable ATP-dependent RNA helicase DDX53, also known by its alternative names such as Cancer-associated gene protein, Cancer/testis antigen 26, and DEAD box protein 53, plays a crucial role in RNA processing mechanisms. This protein belongs to the DEAD box protein family, characterized by their conserved motif Asp-Glu-Ala-Asp (DEAD), which is essential for their helicase activity.

Therapeutic significance:

Understanding the role of Probable ATP-dependent RNA helicase DDX53 could open doors to potential therapeutic strategies. Its involvement in RNA processing suggests its potential impact on gene expression regulation, which is a critical area in cancer research and therapy development.