Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

Our detailed focused library is generated on demand with advanced virtual screening and parameter assessment technology powered by the Receptor.AI drug discovery platform. This method surpasses traditional approaches, delivering compounds of better quality with enhanced 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.


In the library, a selection of top modulators is provided, each marked with 38 ADME-Tox and 32 parameters related to physicochemical properties and drug-likeness. Also, every compound comes with its best docking poses, affinity scores, and activity scores, providing a comprehensive overview.


We utilise our cutting-edge, exclusive workflow to develop focused libraries for ion channels.


 

Fig. 1. The screening workflow of Receptor.AI

This process includes comprehensive molecular simulations of the ion channel in its native membrane environment, depicting its open, closed, and inactivated states, and ensemble virtual screening that accounts for conformational mobility in each state. Tentative binding pockets are investigated inside the pore, at the gating region, and in allosteric sites to cover the full spectrum of possible mechanisms of action.


Several key aspects differentiate our library:


  • Receptor.AI compiles an all-encompassing dataset on the target protein, including historical experiments, literature data, known ligands, and structural insights, maximising the chances of prioritising the most pertinent compounds.

  • The platform employs state-of-the-art molecular simulations to identify potential binding sites, ensuring the focused library is primed for discovering allosteric inhibitors and binders of concealed pockets.

  • Over 50 customisable AI models, thoroughly evaluated in various drug discovery endeavours and research projects, make Receptor.AI both efficient and accurate. This technology is integral to the development of our focused libraries.

  • In addition to generating focused libraries, Receptor.AI offers a full range of services and solutions for every step of preclinical drug discovery, with a pricing model based on success, thereby reducing risk and promoting joint project success.


PARTNER
Receptor.AI
 
UPACC
O43497

UPID:
CAC1G_HUMAN

ALTERNATIVE NAMES:
Cav3.1c; NBR13; Voltage-gated calcium channel subunit alpha Cav3.1

ALTERNATIVE UPACC:
O43497; D6RA64; E7EPR0; O43498; O94770; Q19QY8; Q19QY9; Q19QZ0; Q19QZ1; Q19QZ2; Q19QZ3; Q19QZ4; Q19QZ5; Q19QZ6; Q19QZ7; Q19QZ8; Q19QZ9; Q19R00; Q19R01; Q19R02; Q19R03; Q19R04; Q19R05; Q19R06; Q19R07; Q19R08; Q19R09; Q19R10; Q19R11; Q19R12; Q19R13; Q19R15; Q19R16; Q19R17; Q19R18; Q2TAC4; Q9NYU4; Q9NYU5; Q9NYU6; Q9NYU7; Q9NYU8; Q9NYU9; Q9NYV0; Q9NYV1; Q9UHN9; Q9UHP0; Q9ULU6; Q9UNG7; Q9Y5T2; Q9Y5T3

BACKGROUND:
Voltage-dependent T-type calcium channel subunit alpha-1G, also referred to as Cav3.1c, is crucial for the entry of calcium ions in excitable cells, affecting muscle contraction, hormone release, and cell motility. It generates T-type calcium currents, characterized by activation at negative potentials, playing a key role in the modulation of neuronal firing patterns and supporting calcium signaling in various tissues.

THERAPEUTIC SIGNIFICANCE:
Given its involvement in Spinocerebellar ataxia 42 and its severe, early-onset variant, research into Voltage-dependent T-type calcium channel subunit alpha-1G offers promising avenues for therapeutic intervention. Targeting this protein's pathway could potentially mitigate the progression of these genetic disorders.

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