Focused On-demand Library for Fibronectin

Available from Reaxense
Predicted by Alphafold

Focused On-demand Libraries - Reaxense Collaboration

Explore the Potential with AI-Driven Innovation

This extensive focused library is tailor-made using the latest virtual screening and parameter assessment technology, operated by the Receptor.AI drug discovery platform. This technique is more effective than traditional methods, offering compounds with improved activity, selectivity, and safety.

The compounds are cherry-picked from the vast virtual chemical space of over 60B molecules. The synthesis and delivery of compounds is facilitated by our partner 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 protein-protein interfaces.

 Fig. 1. The sreening workflow of Receptor.AI

The method includes extensive molecular simulations of the target protein alone and in complex with its most relevant partner proteins, followed by ensemble virtual screening that considers conformational mobility in both free and complex states. Potential binding pockets are examined on the protein-protein interaction interface and in distant allosteric sites to cover all possible mechanisms of action.

Our library is unique due to several crucial aspects:

  • Receptor.AI compiles all relevant data on the target protein, such as past experimental results, literature findings, known ligands, and structural data, thereby enhancing the likelihood of focusing on the most significant compounds.
  • By utilizing advanced molecular simulations, the platform is adept at locating potential binding sites, rendering the compounds in the focused library well-suited for unearthing allosteric inhibitors and binders for hidden pockets.
  • The platform is supported by more than 50 highly specialized AI models, all of which have been rigorously tested and validated in diverse drug discovery and research programs. Its design emphasizes efficiency, reliability, and accuracy, crucial for producing focused libraries.
  • Receptor.AI extends beyond just creating focused libraries; it offers a complete spectrum of services and solutions during the preclinical drug discovery phase, with a success-dependent pricing strategy that reduces risk and fosters shared success in the project.







Alternative names:

Cold-insoluble globulin

Alternative UPACC:

P02751; B7ZLF0; E9PE77; E9PG29; O95609; O95610; Q14312; Q14325; Q14326; Q17RV7; Q53S27; Q564H7; Q585T2; Q59EH1; Q60FE4; Q68DP8; Q68DP9; Q68DT4; Q6LDP6; Q6MZS0; Q6MZU5; Q6N025; Q6N0A6; Q7Z391; Q86T27; Q8IVI8; Q96KP7; Q96KP8; Q96KP9; Q9H1B8; Q9HAP3; Q9UMK2


Fibronectin, also known as Cold-insoluble globulin, plays a pivotal role in various biological processes including cell adhesion, wound healing, and cell motility. It binds to cell surfaces and several compounds like collagen and DNA, facilitating cell shape maintenance and opsonization. Fibronectin's involvement in osteoblast mineralization and collagen deposition underscores its significance in tissue repair and development.

Therapeutic significance:

Linked to diseases such as Glomerulopathy with fibronectin deposits 2 and Spondylometaphyseal dysplasia, corner fracture type, Fibronectin's study offers insights into novel therapeutic approaches. Its role in inhibiting tumor growth and promoting insulin sensitization further highlights its potential in disease treatment and management strategies.

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