Molecular property prediction is essential in a variety of contemporary scientific fields, such as drug development and the design of energy storage materials. Although there are many machine learning models available for this purpose, those that achieve high accuracy while also offering the interpretability of predictions are uncommon. We present a graph neural network that not only matches the prediction accuracies of leading models but also provides insights into four levels of molecular substructures. This model helps identify which atoms, bonds, molecular fragments, and connections among fragments are significant for predicting a specific molecular property. Understanding the importance of connections between fragments is particularly valuable for molecules with substructures that do not connect through standard bonds. The model additionally can quantify the impact of specific fragments on the prediction, allowing for the identification of fragments that may improve or degrade a property value. These interpretable features are essential for deriving scientific insights from the model's learned relationships between molecular structures and properties.
Panapitiya et al. (Wed,) studied this question.