A Dual-Modality Complex-Valued Fusion Method for Predicting Side Effects of Drug-Drug Interactions Based on Graph Neural Network

Predicting potential side effects of drug-drug interactions (DDIs), which is a major concern in clinical treatment, can increase therapeutic efficacy. In recent studies, how to use the multi-modal drug features is important for DDI prediction. Thus, it remains a challenge to explore an efficient computational method to achieve the feature fusion cross- and intra-modality. In this paper, we propose a dual-modality complex-valued fusion method (DMCF-DDI) for predicting the side effects of DDIs, using the form and properties of complex-vector to enhance the representations of DDIs. Firstly, DMCF-DDI applies two Graph Convolutional Network (GCN) encoders to learn molecular structure and topological features from fingerprint and knowledge graphs, respectively. Secondly, an asymmetric skip connection (ASC) uses distinct semantic-level features to construct the complex-valued drug pair representations (DPRs). Then, the complex-vector multiplication is used as a fusion operator to obtain the fine-grained DPRs. Finally, we calculate the prediction probability of DDIs by Hermitian inner product in the complex space. Compared with other methods, DMCF-DDI achieves superior performance in all situations using a fusion operator with the lowest parameter numbers. For the case study, we select six diseases and common side effects in clinical treatment to verify identification ability of our model. We also prove the advantage of ASC and complex-valued fusion can achieve to align the cross-modal fused positive DPRs through a comprehensive analysis on the phase-modulus distribution histogram of DPRs. In the end, we explain the reason for alignment based on the similarity of features and node neighbors.