The method includes extensive molecular simulations of the target protein alone and in complex with its most relevant partner proteins, followed by ensemble virtual screening that considers conformational mobility in both free and complex states. Potential binding pockets are examined on the protein-protein interaction interface and in distant allosteric sites to cover all 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.