DDI-LLM: Predicting unseen Drug–Drug Interactions using Large Language Models and molecular graphs

This article introduces DDI-LLM, a hybrid pipeline that combines open-source Large Language Models (MedGemma) with molecular graph representations for the prediction of Drug–Drug Interactions (DDIs) of new compounds. The method integrates SMILES-based structural embeddings with literature-derived semantic embeddings, allowing the model to use molecular as well as textual information for DDI prediction. Moreover, a cross-attention interaction module is implemented to integrate these two types of embeddings efficiently. The experiments based on DrugBank v5.1.10 and PubChem BioAssay show that the model has better generalization and achieves higher (Precision, Recall, F1-score, and AUC) than DeepDDI, MolTrans, and BioBERT-DDI which are taken as baselines. Additionally, we evaluate DDI-LLM using a novel Cold-Start Precision metric, with a precision of 0.80 for novel drug pairs, this being a limiting factor in drug safety in the real world. This study advances DDI prediction methodologies and has significant clinical implications for drug safety and therapy optimization.