Focused On-demand Library for Transforming growth factor-beta-induced protein ig-h3

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.

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 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 utilise our cutting-edge, exclusive workflow to develop 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.







Alternative names:

Kerato-epithelin; RGD-containing collagen-associated protein

Alternative UPACC:

Q15582; D3DQB1; O14471; O14472; O14476; O43216; O43217; O43218; O43219; Q53XM1


Transforming growth factor-beta-induced protein ig-h3, also known as Kerato-epithelin and RGD-containing collagen-associated protein, plays a crucial role in cell adhesion and potentially in cell-collagen interactions. This protein's involvement in the structural integrity and function of the cornea is underscored by its association with various forms of corneal dystrophies.

Therapeutic significance:

Given its pivotal role in corneal dystrophies such as epithelial basement membrane dystrophy, Groenouw type 1, lattice type 1 and 3A, Thiel-Behnke type, Reis-Bucklers type, and Avellino type, understanding the function of Transforming growth factor-beta-induced protein ig-h3 could pave the way for innovative therapeutic strategies targeting these debilitating eye diseases.

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