AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Hemoglobin subunit gamma-1

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.

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.

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

By deploying molecular simulations, our approach comprehensively covers a broad array of proteins, tracking their flexibility and dynamics individually and within complexes. Ensemble virtual screening is utilised to take into account conformational dynamics, identifying pivotal binding sites located within functional regions and at allosteric locations. This thorough exploration ensures that every conceivable mechanism of action is considered, aiming to identify new therapeutic targets and advance lead compounds throughout a vast 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

P69891

UPID:

HBG1_HUMAN

Alternative names:

Gamma-1-globin; Hb F Agamma; Hemoglobin gamma-1 chain; Hemoglobin gamma-A chain

Alternative UPACC:

P69891; P02096; P62027; Q549G1; Q8TDA1; Q96FH7

Background:

Hemoglobin subunit gamma-1, also known as Gamma-1-globin, Hb F Agamma, Hemoglobin gamma-1 chain, and Hemoglobin gamma-A chain, plays a pivotal role in the composition of fetal hemoglobin F, alongside alpha chains. This protein is essential for carrying oxygen from the mother to the fetus during pregnancy.

Therapeutic significance:

Understanding the role of Hemoglobin subunit gamma-1 could open doors to potential therapeutic strategies. Its critical function in fetal development highlights its importance in medical research and drug discovery.

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