AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Hemoglobin subunit beta

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 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 promising modulators annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Also, each compound is presented with its optimal docking poses, affinity scores, and activity scores, providing a comprehensive overview.

We utilise our cutting-edge, exclusive workflow to develop 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.

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

P68871

UPID:

HBB_HUMAN

Alternative names:

Beta-globin; Hemoglobin beta chain

Alternative UPACC:

P68871; A4GX73; B2ZUE0; P02023; Q13852; Q14481; Q14510; Q45KT0; Q549N7; Q6FI08; Q6R7N2; Q8IZI1; Q9BX96; Q9UCD6; Q9UCP8; Q9UCP9

Background:

Hemoglobin subunit beta, also known as Beta-globin, plays a crucial role in oxygen transport from the lungs to peripheral tissues. It is part of the hemoglobin molecule, which carries oxygen in the blood. Additionally, Beta-globin is involved in regulating blood pressure through LVV-hemorphin-7, which potentiates the activity of bradykinin. It also functions as an endogenous inhibitor of enkephalin-degrading enzymes and as a selective antagonist of the P2RX3 receptor, implicating it in pain and inflammation regulation.

Therapeutic significance:

Mutations in the Hemoglobin subunit beta gene are linked to several blood disorders, including Heinz body anemias, Beta-thalassemia, Sickle cell disease, and Beta-thalassemia, dominant, inclusion body type. These conditions highlight the protein's critical role in maintaining healthy red blood cell function and structure. Understanding the molecular mechanisms of these diseases offers potential pathways for developing targeted therapies, emphasizing the importance of Hemoglobin subunit beta in medical research.

Looking for more information on this library or underlying technology? Fill out the form below and we'll be in touch with all the details you need.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.