Fuzzy-DDI: A robust fuzzy logic query model for complex drug-drug interaction prediction

Drug-drug interactions (DDI) refer to the compound effects that occur when patients take multiple drugs simultaneously, which may reduce the drug efficacy and even harm the patient's health. Therefore, DDI prediction is significant for drug development and safe medication. Despite the great efforts of researchers, existing methods mainly focus on predicting interactions between drug pairs, cannot contain more biomedical information, and have poor robustness, limiting their application in real-world scenarios. Therefore, we propose a new robust fuzzy logic query model, Fuzzy-DDI, to predict DDI under various complex conditions. Specifically, Fuzzy-DDI decomposes DDI predictions into relational projections and logical operations on rough sets during inference. Fuzzy logic makes it more fault-tolerant than binary logic models. We explore the reasoning ability of the model in a more realistic and meaningful DDI prediction task with target cell type information and explore the robustness of Fuzzy-DDI in noisy environments and missing sample environments. Experiments on three benchmark datasets show that Fuzzy-DDI significantly outperforms state-of-the-art methods on various DDI prediction tasks, demonstrating its capabilities in inference and robustness. The data and code are available at https://github.com/Cheng0829/Fuzzy-DDI.