AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Retinoblastoma-like protein 2

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.

We carefully select specific compounds from a vast collection of over 60 billion molecules in virtual chemical space. Our partner Reaxense helps in synthesizing and delivering these compounds.

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

 Fig. 1. The sreening workflow of Receptor.AI

Our strategy employs molecular simulations to explore an extensive range of proteins, capturing their dynamics both individually and within complexes with other proteins. Through ensemble virtual screening, we address proteins' conformational mobility, uncovering key binding sites at both functional regions and remote allosteric locations. This comprehensive investigation ensures a thorough assessment of all potential mechanisms of action, with the goal of discovering innovative therapeutic targets and lead molecules across across diverse biological functions.

Our library is unique due to several crucial aspects:

  • Receptor.AI compiles all relevant data on the target protein, such as past experimental results, literature findings, known ligands, and structural data, thereby enhancing the likelihood of focusing on the most significant compounds.
  • By utilizing advanced molecular simulations, the platform is adept at locating potential binding sites, rendering the compounds in the focused library well-suited for unearthing allosteric inhibitors and binders for hidden pockets.
  • The platform is supported by more than 50 highly specialized AI models, all of which have been rigorously tested and validated in diverse drug discovery and research programs. Its design emphasizes efficiency, reliability, and accuracy, crucial for producing focused libraries.
  • Receptor.AI extends beyond just creating focused libraries; it offers a complete spectrum of services and solutions during the preclinical drug discovery phase, with a success-dependent pricing strategy that reduces risk and fosters shared success in the project.

partner

Reaxense

upacc

Q08999

UPID:

RBL2_HUMAN

Alternative names:

130 kDa retinoblastoma-associated protein; Retinoblastoma-related protein 2; pRb2

Alternative UPACC:

Q08999; B7Z913; Q15073; Q16084; Q8NE70; Q92812

Background:

Retinoblastoma-like protein 2, also known as pRb2, plays a pivotal role in cell division, chromatin structure maintenance, and transcriptional repression. It is a key regulator, ensuring the stability of histone methylation and the formation of constitutive heterochromatin. By recruiting histone methyltransferases KMT5B and KMT5C, pRb2 exerts epigenetic control over gene expression. Its interaction with E2F5 and cyclins A and E underscores its significance in cell cycle regulation and potentially in the adenovirus E1A protein's transforming capacity.

Therapeutic significance:

Linked to Brunet-Wagner neurodevelopmental syndrome, Retinoblastoma-like protein 2's genetic variants underscore its clinical relevance. Understanding its role could unveil novel therapeutic strategies for managing severe developmental and intellectual disabilities associated with this syndrome.

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