Substructure-guided Deep Graph Learning in Molecular Toxicity Prediction

Computational toxicity prediction has become a key component in modern drug discovery. Although machine learning or deep learning techniques have reformed this field in recent years, more in-depth studies on addressing data imbalance, missing labels, and lack of model interpretability are still needed. In this work, we develop a substructure-based deep graph learning architecture, by introducing various functional groups into the construction of molecular graphs and handling them through deep learning models. Our model, with several strategies adopted to deal with the missing labels and class imbalance in the datasets, performed well in toxicity prediction tasks. A functional group-based feature importance analysis provided further insights into different toxicity predictions and improved the interpretability of our model. It provides a solid foundation for the development of reliable toxicity prediction tools and supports rational decision-making in the drug development process.