MMDDI-MGPFF: Multi-Modal Drug Representation Learning with Molecular Graph and Pharmacological Feature Fusion for Drug-Drug Interaction Event Prediction

Drug-drug interactions pose a significant challenge in healthcare, directly impacting patient safety and treatment efficacy. Although recent advances in computational methods have improved drug-drug interaction (DDI) event prediction, many existing models face difficulties in effectively fusing features across different DDI tasks. To address these limitations, we introduce MMDDI-MGPFF, a novel multi-modal drug representation learning framework that integrates molecular graphs and pharmacological feature fusion to improve DDI prediction. Our model leverages a graph isomorphism network (GIN) for efficient encoding of molecular structures, coupled with an autoencoder for learning sequence-based drug features, encompassing both biological and pharmacological characteristics. We propose a multi-modal fusion approach that employs multi-head attention and deep neural networks to seamlessly integrate these graph and sequence modalities. Comprehensive experiments on a benchmark dataset demonstrate that MMDDI-MGPFF significantly outperforms state-of-the-art methods in DDI prediction tasks. Ablation studies further corroborate the efficacy of our model’s components, particularly the GIN and the integration of multi-feature drug representations. This work advances the field of DDI prediction by providing a more holistic and accurate approach to drug representation and interaction modeling.