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Enhancing property and activity prediction and interpretation using multiple molecular graph representations with MMGX - Communications Chemistry
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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 aligns with chemical intuition.
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