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

Focused On-demand Libraries - Receptor.AI 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 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 receptors.


 

Fig. 1. The screening workflow of Receptor.AI

It features thorough molecular simulations of the receptor within its native membrane environment, complemented by ensemble virtual screening that considers its conformational mobility. For dimeric or oligomeric receptors, the full functional complex is constructed, and tentative binding sites are determined on and between the subunits to cover the entire spectrum of potential mechanisms of action.


Several key aspects differentiate our library:


  • Receptor.AI compiles an all-encompassing dataset on the target protein, including historical experiments, literature data, known ligands, and structural insights, maximising the chances of prioritising the most pertinent compounds.

  • The platform employs state-of-the-art molecular simulations to identify potential binding sites, ensuring the focused library is primed for discovering allosteric inhibitors and binders of concealed pockets.

  • Over 50 customisable AI models, thoroughly evaluated in various drug discovery endeavours and research projects, make Receptor.AI both efficient and accurate. This technology is integral to the development of our focused libraries.

  • In addition to generating focused libraries, Receptor.AI offers a full range of services and solutions for every step of preclinical drug discovery, with a pricing model based on success, thereby reducing risk and promoting joint project success.

PARTNER
Receptor.AI
 
UPACC
P05019

UPID:
IGF1_HUMAN

ALTERNATIVE NAMES:
Mechano growth factor; Somatomedin-C

ALTERNATIVE UPACC:
P05019; B2RWM7; E9PD02; P01343; Q14620

BACKGROUND:
Insulin-like growth factor I, recognized for its higher growth-promoting activity compared to insulin, is essential for physiological regulation of glucose transport and glycogen synthesis. It facilitates glucose uptake in osteoblastic cells and plays a crucial role in synapse maturation and sensory perception of smell. The protein's interaction with integrins and IGF1R is vital for its signaling and biological functions.

THERAPEUTIC SIGNIFICANCE:
Given its involvement in Insulin-like growth factor I deficiency, characterized by significant growth, auditory, and cognitive impairments, Insulin-like growth factor I emerges as a key target in drug discovery. Exploring its functions and mechanisms offers promising avenues for developing treatments for a range of disorders, including metabolic, developmental, and neurological diseases.

Looking for more information on this library or underlying technology? Fill out the form below and we will be in touch with all the details you need.


Page 252 of 8789