Focused On-demand Libraries - Receptor.AI Collaboration
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.
From a virtual chemical space containing more than 60 billion molecules, we precisely choose certain compounds. Reaxense aids in their synthesis and provision.
The library features a range of promising modulators, each detailed with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Plus, each compound is presented with its ideal docking poses, affinity scores, and activity scores, ensuring a thorough insight.
We use our state-of-the-art dedicated workflow for designing focused libraries for ion channels.
It includes extensive molecular simulations of the channel in its native membrane environment in open, closed and inactivated forms and the ensemble virtual screening accounting for conformational mobility in each of these states. Tentative binding pockets are considered inside the pore, in the gating region and in the allosteric locations to cover the whole spectrum of possible mechanisms of action.
Key features that set our library apart include:
- The Receptor.AI platform integrates extensive information about the target protein, such as historical experiments, academic research, known ligands, and structural insights, thereby increasing the likelihood of identifying highly relevant compounds.
- The platform’s sophisticated molecular simulations are designed to discover potential binding sites, ensuring that our focused library is optimal for the discovery of allosteric inhibitors and binders for cryptic pockets.
- With over 50 customisable AI models, verified through extensive testing in commercial drug discovery and research, Receptor.AI is efficient, reliable, and precise. These models are essential in the production of our focused libraries.
- Receptor.AI not only produces focused libraries but also provides full services and solutions at every stage of preclinical drug discovery, with a success-based pricing structure that aligns our interests with the success of your project.
Receptor.AI
Q12809
UPID:
KCNH2_HUMAN
ALTERNATIVE NAMES:
Eag homolog; Ether-a-go-go-related gene potassium channel 1; Voltage-gated potassium channel subunit Kv11.1
ALTERNATIVE UPACC:
Q12809; A5H1P7; C4PFH9; D3DX04; O75418; O75680; Q708S9; Q9BT72; Q9BUT7; Q9H3P0