Explore the Potential with AI-Driven Innovation
This comprehensive focused library is produced on demand with state-of-the-art virtual screening and parameter assessment technology driven by Receptor.AI drug discovery platform. This approach outperforms traditional methods and provides higher-quality compounds with superior 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.
Contained in the library are leading modulators, each labelled with 38 ADME-Tox and 32 physicochemical and drug-likeness qualities. In addition, each compound is illustrated with its optimal docking poses, affinity scores, and activity scores, giving a complete picture.
We employ our advanced, specialised process to create targeted libraries.
Fig. 1. The sreening workflow of Receptor.AI
Utilising molecular simulations, our approach thoroughly examines a wide array of proteins, tracking their conformational changes individually and within complexes. Ensemble virtual screening enables us to address conformational flexibility, revealing essential binding sites at functional regions and allosteric locations. Our rigorous analysis guarantees that no potential mechanism of action is overlooked, aiming to uncover new therapeutic targets and lead compounds across diverse biological functions.
Key features that set our library apart include:
partner
Reaxense
upacc
P12109
UPID:
CO6A1_HUMAN
Alternative names:
-
Alternative UPACC:
P12109; O00117; O00118; Q14040; Q14041; Q16258; Q7Z645; Q9BSA8
Background:
Collagen alpha-1(VI) chain, encoded by the gene with accession number P12109, plays a pivotal role as a cell-binding protein. This protein is integral to the structure and function of connective tissues.
Therapeutic significance:
Linked to Bethlem myopathy 1 and Ullrich congenital muscular dystrophy 1, Collagen alpha-1(VI) chain's understanding could pave the way for innovative treatments targeting these genetic disorders.