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Interpreting Graph Neural Networks with Myerson Values for Cheminformatics Approaches

By Samuel K. R. Homberg and others
Graph neural networks (GNNs) are a natural choice to represent chemical data, due to their inherent ability to handle arbitrary input topologies. They avoid the need to convert molecules into molecular fingerprints with a fixed vector length. However, like most deep learning models, GNNs are not interpretable and common explainability... Show more
July 9, 2024
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Interpreting Graph Neural Networks with Myerson Values for Cheminformatics Approaches
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