AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Retinoblastoma-associated protein

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

The specialised, focused library is developed on demand with the most recent virtual screening and parameter assessment technology, guided by the Receptor.AI drug discovery platform. This approach exceeds the capabilities of traditional methods and offers compounds with higher 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.

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 enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

It includes comprehensive molecular simulations of the catalytic and allosteric binding pockets and the ensemble virtual screening accounting for their conformational mobility. In the case of designing modulators, the structural changes induced by reaction intermediates are taken into account to leverage 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

P06400

UPID:

RB_HUMAN

Alternative names:

p105-Rb; p110-RB1; pRb; pp110

Alternative UPACC:

P06400; A8K5E3; P78499; Q5VW46; Q8IZL4

Background:

The Retinoblastoma-associated protein, known as pRb, plays a pivotal role in cell cycle regulation, specifically the G1/S transition. It functions as a tumor suppressor by inhibiting E2F-responsive genes, crucial for DNA replication and cell division. pRb's activity is modulated through phosphorylation, with its hypophosphorylated form repressing gene transcription by binding to E2F transcription regulators and recruiting chromatin-modifying enzymes. Additionally, pRb contributes to heterochromatin formation and chromatin structure maintenance, highlighting its significance in genomic stability.

Therapeutic significance:

pRb's dysfunction is linked to several malignancies, including retinoblastoma, bladder cancer, and osteogenic sarcoma. Its role in the cell cycle and tumor suppression makes it a critical target for cancer therapy. Understanding the mechanisms of pRb's action and its disruption in cancer can lead to innovative therapeutic strategies, potentially offering new avenues for treatment of these diseases.

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