The approach involves in-depth molecular simulations of the target protein by itself and in complex with its primary partner proteins, paired with ensemble virtual screening that factors in conformational mobility in both the unbound and complex states. The tentative binding pockets are identified at the protein-protein interaction interface and in distant allosteric areas, aiming to capture the full range of mechanisms of action.

Our library distinguishes itself through several key aspects:

• The Receptor.AI platform integrates all available data about the target protein, including past experiments, literature data, known ligands, structural information and more. This consolidated approach maximises the probability of prioritising highly relevant compounds.


• The platform uses sophisticated molecular simulations to identify possible binding sites so that the compounds in the focused library are suitable for discovering allosteric inhibitors and the binders for cryptic pockets.


• The platform integrates over 50 highly customisable AI models, which are thoroughly tested and validated on a multitude of commercial drug discovery programs and research projects. It is designed to be efficient, reliable and accurate. All this power is utilised when producing the focused libraries.


• In addition to producing the focused libraries, Receptor.AI provides services and end-to-end solutions at every stage of preclinical drug discovery. The pricing model is success-based, which reduces your risks and leverages the mutual benefits of the project's success.