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.
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 promising modulators annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Also, each compound is presented with its optimal docking poses, affinity scores, and activity scores, providing a comprehensive overview.
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
Q96FQ6
UPID:
S10AG_HUMAN
Alternative names:
Aging-associated gene 13 protein; Protein S100-F; S100 calcium-binding protein A16
Alternative UPACC:
Q96FQ6; A8K439; D3DV52; Q5RHS6
Background:
Protein S100-A16, also known as Aging-associated gene 13 protein and S100 calcium-binding protein A16, plays a crucial role in cellular processes by binding calcium ions. Its ability to promote adipocyte differentiation and influence preadipocyte proliferation and adipogenesis highlights its significance in metabolic regulation. Furthermore, its impact on insulin-stimulated glucose uptake underscores its potential in metabolic disease research.
Therapeutic significance:
Understanding the role of Protein S100-A16 could open doors to potential therapeutic strategies. Its involvement in adipogenesis and glucose metabolism presents a promising avenue for addressing metabolic disorders.