Genesis has developed Dynamic PotentialNet as a proprietary approach to predict potency, selectivity, and ADMET. This technology provides GEMS with unprecedented speed and accuracy in property predictions, including for data-poor and previously undruggable targets.
Dynamic PotentialNet represents the 3D structure and interactions of protein-ligand complexes as spatial graphs. By training on a combination of molecular simulations and experimental data, it learns representations of the physical interactions underlying binding affinity. This enables the model to generalize to novel protein targets and make accurate predictions based on 3D structures alone, without requiring on-target binding affinity data.