Prediction and Interpretable Visualization of Retrosynthetic Reactions Using Graph Convolutional Networks

Recently, many research groups have been addressing data-driven
approaches for (retro)­synthetic reaction prediction and retrosynthetic
analysis. Although the performances of the data-driven approach have
progressed because of recent advances of machine learning and deep
learning techniques, problems such as improving capability of reaction
prediction and the black-box problem of neural networks persist for
practical use by chemists. To spread data-driven approaches to chemists,
we focused on two challenges: improvement of retrosynthetic reaction
prediction and interpretability of the prediction. In this paper,
we propose an interpretable prediction framework using graph convolutional
networks (GCN) for retrosynthetic reaction prediction and integrated
gradients (IG) for visualization of contributions to the prediction
to address these challenges. As a result, from the viewpoint of balanced
accuracies, our model showed better performances than the approach
using an extended-connectivity fingerprint. Furthermore, IG-based
visualization of the GCN prediction successfully highlighted reaction-related
atoms.