AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Roundabout homolog 2

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

This extensive focused library is tailor-made using the latest virtual screening and parameter assessment technology, operated by the Receptor.AI drug discovery platform. This technique is more effective than traditional methods, offering compounds with improved 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.

We use our state-of-the-art dedicated workflow for designing focused libraries for protein-protein interfaces.

 Fig. 1. The sreening workflow of Receptor.AI

It includes extensive molecular simulations of the target alone and in complex with its most relevant partner proteins, followed by ensemble virtual screening that accounts for conformational mobility in free and bound forms. The tentative binding pockets are considered on the protein-protein interface itself and in remote allosteric locations in order to cover the whole spectrum of possible mechanisms of action.

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

Q9HCK4

UPID:

ROBO2_HUMAN

Alternative names:

-

Alternative UPACC:

Q9HCK4; O43608; Q19AB4; Q19AB5

Background:

Roundabout homolog 2, encoded by the gene with accession number Q9HCK4, serves as a receptor for SLIT2 and likely SLIT1. These ligands are pivotal in cellular migration, including axonal navigation during neural development and axonal projection to various regions.

Therapeutic significance:

Vesicoureteral reflux 2, a congenital anomaly of the kidney and urinary tract, is associated with mutations affecting Roundabout homolog 2. Understanding its role could lead to novel therapeutic strategies for managing this condition and preventing its complications, such as urinary tract infections and renal scarring.

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