Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity
This work addresses the challenge of interpretability for medicinal chemists using graph neural networks, though it is incremental as it builds on existing methods with new regularizers.
The authors tackled the problem of improving explainability in molecular graph neural networks by proposing two regularization techniques, Batch Representation Orthonormalization and Gini regularization, which enhanced attribution accuracy on benchmark datasets and were preferred by medicinal chemists in real-world applications.
Rationalizing which parts of a molecule drive the predictions of a molecular graph convolutional neural network (GCNN) can be difficult. To help, we propose two simple regularization techniques to apply during the training of GCNNs: Batch Representation Orthonormalization (BRO) and Gini regularization. BRO, inspired by molecular orbital theory, encourages graph convolution operations to generate orthonormal node embeddings. Gini regularization is applied to the weights of the output layer and constrains the number of dimensions the model can use to make predictions. We show that Gini and BRO regularization can improve the accuracy of state-of-the-art GCNN attribution methods on artificial benchmark datasets. In a real-world setting, we demonstrate that medicinal chemists significantly prefer explanations extracted from regularized models. While we only study these regularizers in the context of GCNNs, both can be applied to other types of neural networks