Focused On-demand Library for eIF-2-alpha kinase GCN2

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.

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.

In the library, a selection of top modulators is provided, each marked with 38 ADME-Tox and 32 parameters related to physicochemical properties and drug-likeness. Also, every compound comes with its best docking poses, affinity scores, and activity scores, providing a comprehensive overview.

We utilise our cutting-edge, exclusive workflow to develop focused libraries for enzymes.

 Fig. 1. The sreening workflow of Receptor.AI

This approach involves comprehensive molecular simulations of the catalytic and allosteric binding pockets and ensemble virtual screening that accounts for their conformational flexibility. In the case of designing modulators, the structural adjustments caused by reaction intermediates are considered to improve 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.







Alternative names:

Eukaryotic translation initiation factor 2-alpha kinase 4; GCN2-like protein

Alternative UPACC:

Q9P2K8; C9JEC4; Q69YL7; Q6DC97; Q96GN6; Q9H5K1; Q9NSQ3; Q9NSZ5; Q9UJ56


The eIF-2-alpha kinase GCN2, also known as Eukaryotic translation initiation factor 2-alpha kinase 4 or GCN2-like protein, is a metabolic-stress sensing protein kinase. It plays a crucial role in phosphorylating EIF2S1/eIF-2-alpha under conditions of low amino acid availability, initiating the integrated stress response (ISR) for adaptation to amino acid starvation. This kinase is involved in various cellular processes, including cell cycle arrest, synaptic plasticity, neurite outgrowth inhibition, and proapoptotic responses to glucose deprivation.

Therapeutic significance:

Pulmonary venoocclusive disease 2, an autosomal recessive disorder characterized by severe pulmonary hypertension, is linked to variants affecting the GCN2 gene. Understanding the role of eIF-2-alpha kinase GCN2 could open doors to potential therapeutic strategies for this devastating condition.

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