MFCN-DDI: Capsule network based on multimodal feature for multitype drug-drug interaction prediction

Precise prediction of drug-drug interactions (DDIs) is essential for pharmaceutical research and clinical applications to minimize adverse reactions, optimize therapies, and reduce costs. However, existing methods still face challenges in effectively integrating multidimensional drug features and fully utilizing edge features in molecular graphs, which are crucial for predicting DDIs precisely. Moreover, current methods may not adequately capture the complex relationships between different types of features, limiting predictive performance. This paper proposes the MFCN-DDI model for DDI type prediction. The model consists of a multimodal feature extraction module, a capsule network-based feature fusion module, and a DDI predictor module. In the multimodal feature extraction module, four kinds of features are used to provide rich and comprehensive representations for subsequent DDI type prediction, where molecular graph features are generated by considering molecular graphs with edge features. The capsule network-based feature fusion module captures complex feature relationships to generate high-quality integrated representations. In the DDI predictor module, multiclass and multilabel classification predictions are performed accurately. Experimental results show that MFCN-DDI outperforms existing comparison models in prediction tasks. Case studies further prove its practical applicability. In summary, MFCN-DDI provides an efficient and reliable solution for DDI prediction.