IMF-DDI: Information Mapping and Fusion Framework for Drug-drug Interaction Prediction

Drug-drug interactions (DDIs) are crucial throughout various stages of drug development. Using computer-aided methods for accurate prediction of DDIs can enhance clinical safety and accelerate drug discovery. However, most existing deep learning methods heavily rely on the connectivity information between drugs. The neglect of the large number of potential DDI relationships can hinder the model's ability to extract meaningful information, thereby limiting its generalization capacity. To address these limitations, we propose IMF-DDI, an innovative DDI prediction framework that obtains drug molecule representations for DDI prediction by combining information from multiple external entities. First, our proposed information mapping module enables the model to capture the associations between drug molecules in terms of their interactions with multiple external entities. Meanwhile, the multi-source information fusion module efficiently integrates information from multiple external entities to generate the final representations of drug molecules. We carefully designed three distinct experimental tasks to validate the effectiveness of IMF-DDI. Our method establishes the current state-of-the-art across all tasks on the DrugBank dataset, while achieving the best performance in most tasks on the TWOSIDES dataset.