Enhancing property and activity prediction and interpretation using multiple molecular graph representations with MMGX
Abstract Graph Neural Networks (GNNs) excel in compound property and activity prediction, but the choice of molecular graph representations significantly influences model learning and interpretation.While atom-level molecular graphs resemble natural topology, they overlook key substructures or functional groups and their interpretation partially al