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.
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.
We employ our advanced, specialised process to create targeted libraries for enzymes.
Fig. 1. The sreening workflow of Receptor.AI
It includes comprehensive molecular simulations of the catalytic and allosteric binding pockets and the ensemble virtual screening accounting for their conformational mobility. In the case of designing modulators, the structural changes induced by reaction intermediates are taken into account to leverage activity and selectivity.
Our library stands out due to several important features:
partner
Reaxense
upacc
P16519
UPID:
NEC2_HUMAN
Alternative names:
KEX2-like endoprotease 2; Prohormone convertase 2; Proprotein convertase 2
Alternative UPACC:
P16519; B1ANH9; B4DFQ3; Q14927; Q5JYQ1; Q8IWA8; Q9NQG3; Q9NUG1; Q9UJC6
Background:
Neuroendocrine convertase 2, also known as Prohormone convertase 2, plays a crucial role in the processing of hormone and other protein precursors. This serine endopeptidase targets sites comprised of pairs of basic amino acid residues, facilitating the release of glucagon from proglucagon in pancreatic A cells.
Therapeutic significance:
Understanding the role of Neuroendocrine convertase 2 could open doors to potential therapeutic strategies. Its pivotal function in hormone processing underscores its potential as a target for interventions in metabolic disorders.