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Found 3 papers, 2 papers with code
Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity
Rationalizing which parts of a molecule drive the predictions of a molecular graph convolutional neural network (GCNN) can be difficult.
Gini in a Bottleneck: Sparse Molecular Representations for Graph Convolutional Neural Networks
Due to the nature of deep learning approaches, it is inherently difficult to understand which aspects of a molecular graph drive the predictions of the network.
Picasso: A Modular Framework for Visualizing the Learning Process of Neural Network Image Classifiers
Picasso is a free open-source (Eclipse Public License) web application written in Python for rendering standard visualizations useful for analyzing convolutional neural networks.
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