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.
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 utilise our cutting-edge, exclusive workflow to develop focused 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.
Key features that set our library apart include:
partner
Reaxense
upacc
Q9NQL2
UPID:
RRAGD_HUMAN
Alternative names:
-
Alternative UPACC:
Q9NQL2; A8K184; Q7L8F9; Q9NPG0
Background:
Ras-related GTP-binding protein D plays a pivotal role in amino acid-induced activation of the mTORC1 signaling cascade. It forms heterodimeric Rag complexes, cycling between active and inactive forms, crucial for mTORC1 recruitment to lysosomes and activation.
Therapeutic significance:
Linked to Hypomagnesemia 7, a renal disorder with potential cardiomyopathy, understanding Ras-related GTP-binding protein D's role could unveil new therapeutic strategies.