FragNet: A Graph Neural Network for Molecular Property Prediction with Four Levels of Interpretability
This work addresses the need for interpretable models in scientific fields like drug development, offering incremental improvements by combining high accuracy with multi-level interpretability.
The paper tackles molecular property prediction by introducing a graph neural network that matches leading models in accuracy while providing interpretability across four levels of molecular substructures, helping identify significant atoms, bonds, fragments, and connections for specific properties.
Molecular property prediction is essential in a variety of contemporary scientific fields, such as drug development and designing energy storage materials. Although there are many machine learning models available for this purpose, those that achieve high accuracy while also offering 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 on four levels of molecular substructures. This model helps identify which atoms, bonds, molecular fragments, and connections between 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 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.