Prediction of reproductive and developmental toxicity using an attention and gate augmented graph convolutional network

Due to the diverse molecular structures of chemical compounds and their intricate biological pathways of toxicity, predicting their reproductive and developmental toxicity remains a challenge. Traditional Quantitative Structure-Activity Relationship models that rely on molecular descriptors have limitations in capturing the complexity of reproductive and developmental toxicity to achieve high predictive performance. In this study, we developed a descriptor-free deep learning model by constructing a Graph Convolutional Network designed with multi-head attention and gated skip-connections to predict reproductive and developmental toxicity. By integrating structural alerts directly related to toxicity into the model, we enabled more effective learning of toxicologically relevant substructures. We built a dataset of 4,514 diverse compounds, including both organic and inorganic substances. The model was trained and validated using stratified 5-fold cross-validation. It demonstrated excellent predictive performance, achieving an accuracy of 81.19% on the test set. To address the interpretability of the deep learning model, we identified subgraphs corresponding to known structural alerts, providing insights into the model's decision-making process. This study was conducted in accordance with the OECD principles for reliable Quantitative Structure-Activity Relationship modeling and contributes to the development of robust in silico models for toxicity prediction.