AI-ACCELERATED DRUG DISCOVERY

Focused On-demand Library for Epidermal growth factor receptor

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.

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 includes a list of the most effective modulators, each annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Furthermore, each compound is shown with its optimal docking poses, affinity scores, and activity scores, offering a detailed summary.

Our high-tech, dedicated method is applied to construct 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.

Our library distinguishes itself through several key aspects:

  • The Receptor.AI platform integrates all available data about the target protein, including past experiments, literature data, known ligands, structural information and more. This consolidated approach maximises the probability of prioritising highly relevant compounds.
  • The platform uses sophisticated molecular simulations to identify possible binding sites so that the compounds in the focused library are suitable for discovering allosteric inhibitors and the binders for cryptic pockets.
  • The platform integrates over 50 highly customisable AI models, which are thoroughly tested and validated on a multitude of commercial drug discovery programs and research projects. It is designed to be efficient, reliable and accurate. All this power is utilised when producing the focused libraries.
  • In addition to producing the focused libraries, Receptor.AI provides services and end-to-end solutions at every stage of preclinical drug discovery. The pricing model is success-based, which reduces your risks and leverages the mutual benefits of the project's success.

partner

Reaxense

upacc

P00533

UPID:

EGFR_HUMAN

Alternative names:

Proto-oncogene c-ErbB-1; Receptor tyrosine-protein kinase erbB-1

Alternative UPACC:

P00533; O00688; O00732; P06268; Q14225; Q68GS5; Q92795; Q9BZS2; Q9GZX1; Q9H2C9; Q9H3C9; Q9UMD7; Q9UMD8; Q9UMG5

Background:

The Epidermal Growth Factor Receptor (EGFR), also known as Proto-oncogene c-ErbB-1 and Receptor tyrosine-protein kinase erbB-1, plays a pivotal role in cellular signaling pathways. By binding ligands of the EGF family, it activates cascades such as RAS-RAF-MEK-ERK, PI3 kinase-AKT, and others, influencing cell migration, proliferation, and differentiation.

Therapeutic significance:

EGFR's involvement in lung cancer and Inflammatory skin and bowel disease, neonatal, 2, underscores its potential as a therapeutic target. Its role in disease pathogenesis and cell entry facilitation for hepatitis C virus highlights the importance of EGFR modulation in treating these conditions.

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