Drug-induced phospholipidosis (DIPL) is linked to various toxicities, including hepatotoxicity, making it a critical screening factor in the early stages of drug discovery. Several models based on chemical structures have been constructed to predict compounds with DIPL potential. However, most of these models only classify results as inducers or non-inducers, without identifying the specific substructures responsible for positive outcomes. To address this limitation, we constructed an artificial intelligence (AI) model to predict compounds with DIPL potential and visualize structural alerts. The proposed model was constructed using kMoL, an open-source software library that employs a graph neural network approach to learn from chemical structural data. We employed the bagging method, resulting in a model with a high predictive performance. The model attained an F1 score of 0.796 on the external test set. In addition, we used the integrated gradient method to visualize the substructures that contributed to positive predictions. When applying the method to compounds that experimentally conducted structure-activity relationship investigations, the proposed AI model accurately predicted DIPL potential, demonstrating its practical utility in early-stage drug discovery. By predicting DIPL based on the chemical structure of compounds, the proposed model can aid in the screening for DIPL, potentially improving the safety profile of new drug candidates.