Focused On-demand Library for Insulin-like growth factor II

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

Our detailed focused library is generated on demand with advanced virtual screening and parameter assessment technology powered by the Receptor.AI drug discovery platform. This method surpasses traditional approaches, delivering compounds of better quality with enhanced 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.

The library features a range of promising modulators, each detailed with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Plus, each compound is presented with its ideal docking poses, affinity scores, and activity scores, ensuring a thorough insight.

We employ our advanced, specialised process to create targeted libraries for enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

The procedure entails thorough molecular simulations of the catalytic and allosteric binding pockets, accompanied by ensemble virtual screening that factors in their conformational flexibility. When developing modulators, the structural modifications brought about by reaction intermediates are factored in to optimize 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.







Alternative names:

Somatomedin-A; T3M-11-derived growth factor

Alternative UPACC:

P01344; B3KX48; B7WP08; C9JAF2; E3UN45; P78449; Q14299; Q1WM26; Q9UC68; Q9UC69


Insulin-like growth factor II (IGF2), also known as Somatomedin-A, plays a pivotal role in growth and development. It is a major fetal growth hormone in mammals, crucial for fetoplacental development and involved in tissue differentiation. In adults, IGF2 is key in glucose metabolism across various tissues. It acts as a ligand for integrin, essential for IGF2 signaling, and supports muscle differentiation by regulating MYOD1 function.

Therapeutic significance:

IGF2's involvement in Silver-Russell syndrome, characterized by growth retardation and craniofacial features, highlights its therapeutic potential. Understanding IGF2's role could open doors to novel strategies for managing growth-related disorders.

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.