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.
We carefully select specific compounds from a vast collection of over 60 billion molecules in virtual chemical space. Our partner Reaxense helps in synthesizing and delivering these compounds.
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 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.
Key features that set our library apart include:
partner
Reaxense
upacc
Q9NQF3
UPID:
SERHL_HUMAN
Alternative names:
-
Alternative UPACC:
Q9NQF3; Q5JZ95; Q9UH21
Background:
The Serine hydrolase-like protein, identified by the accession number Q9NQF3, is categorized as a putative serine hydrolase. This classification suggests a role in catalyzing the cleavage of peptide bonds in proteins, a critical function in various biological processes.
Therapeutic significance:
Understanding the role of Serine hydrolase-like protein could open doors to potential therapeutic strategies. Its involvement in fundamental biological mechanisms positions it as a key target for drug discovery efforts aimed at addressing a wide range of diseases.