The method involves in-depth molecular simulations of the ion channel in its native membrane environment, including its open, closed, and inactivated states, along with ensemble virtual screening that focuses on conformational mobility for each state. Tentative binding pockets are identified inside the pore, in the gating area, and at allosteric sites to address every conceivable mechanism of action.

Our library is unique due to several crucial aspects:

• Receptor.AI compiles all relevant data on the target protein, such as past experimental results, literature findings, known ligands, and structural data, thereby enhancing the likelihood of focusing on the most significant compounds.


• By utilizing advanced molecular simulations, the platform is adept at locating potential binding sites, rendering the compounds in the focused library well-suited for unearthing allosteric inhibitors and binders for hidden pockets.


• The platform is supported by more than 50 highly specialized AI models, all of which have been rigorously tested and validated in diverse drug discovery and research programs. Its design emphasizes efficiency, reliability, and accuracy, crucial for producing focused libraries.


• Receptor.AI extends beyond just creating focused libraries; it offers a complete spectrum of services and solutions during the preclinical drug discovery phase, with a success-dependent pricing strategy that reduces risk and fosters shared success in the project.

PARTNER
Receptor.AI

 

UPACC
Q12791

UPID:
KCMA1_HUMAN

ALTERNATIVE NAMES:
BK channel; BKCA alpha; Calcium-activated potassium channel, subfamily M subunit alpha-1; K(VCA)alpha; KCa1.1; Maxi K channel; Slo-alpha; Slo1; Slowpoke homolog