AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Protein RCC2

Available from Reaxense
Predicted by Alphafold

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.

From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Our collaborator, Reaxense, aids in their synthesis and provision.

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.

 Fig. 1. The sreening workflow of Receptor.AI

Our methodology employs molecular simulations to explore a wide array of proteins, capturing their dynamic states both individually and within complexes. Through ensemble virtual screening, we address conformational mobility, uncovering binding sites within functional regions and remote allosteric locations. This thorough exploration ensures no potential mechanism of action is overlooked, aiming to discover novel therapeutic targets and lead compounds across an extensive spectrum of biological functions.

Key features that set our library apart include:

  • The Receptor.AI platform integrates extensive information about the target protein, such as historical experiments, academic research, known ligands, and structural insights, thereby increasing the likelihood of identifying highly relevant compounds.
  • The platform’s sophisticated molecular simulations are designed to discover potential binding sites, ensuring that our focused library is optimal for the discovery of allosteric inhibitors and binders for cryptic pockets.
  • With over 50 customisable AI models, verified through extensive testing in commercial drug discovery and research, Receptor.AI is efficient, reliable, and precise. These models are essential in the production of our focused libraries.
  • Receptor.AI not only produces focused libraries but also provides full services and solutions at every stage of preclinical drug discovery, with a success-based pricing structure that aligns our interests with the success of your project.

partner

Reaxense

upacc

Q9P258

UPID:

RCC2_HUMAN

Alternative names:

RCC1-like protein TD-60; Telophase disk protein of 60 kDa

Alternative UPACC:

Q9P258; Q8IVL9; Q9BSN6; Q9NPV8

Background:

Protein RCC2, also known as RCC1-like protein TD-60 or Telophase disk protein of 60 kDa, plays a pivotal role in cell cycle progression and microtubule cytoskeleton organization. It acts as a guanine nucleotide exchange factor for RALA and modulates the activity of small GTPases, such as RAC1, influencing cell division and cytoskeletal arrangements.

Therapeutic significance:

Understanding the role of Protein RCC2 could open doors to potential therapeutic strategies. Its involvement in critical cellular processes underscores its potential as a target in diseases characterized by abnormal cell proliferation and cytoskeletal dysfunctions.

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