Focused On-demand Library for Beta-galactosidase-1-like protein 3

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.

In the library, a selection of top modulators is provided, each marked with 38 ADME-Tox and 32 parameters related to physicochemical properties and drug-likeness. Also, every compound comes with its best docking poses, affinity scores, and activity scores, providing a comprehensive overview.

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

Q8NCI6

UPID:

GLBL3_HUMAN

Alternative names:

Alternative UPACC:

Q8NCI6; A6NEM0; A6NN15; Q6P3S3; Q96FF8

Background:

Beta-galactosidase-1-like protein 3, encoded by the gene with the accession number Q8NCI6, plays a crucial role in cellular processes. Despite its name suggesting a similarity to beta-galactosidase, its specific functions and mechanisms of action in biological systems are subjects of ongoing research. This protein's structure and biochemical pathways, while not fully elucidated, are believed to be significant in maintaining cellular health.

Therapeutic significance:

Understanding the role of Beta-galactosidase-1-like protein 3 could open doors to potential therapeutic strategies. The exploration of its functions and interactions within the cell promises to uncover novel targets for drug development, particularly in diseases where its activity is dysregulated.