SGFNNs: Signed Graph Filtering-based Neural Networks for Predicting Drug-Drug Interactions

Capturing comprehensive information about drug-drug interactions (DDIs) is one of the key tasks in public health and drug development. Recently, graph neural networks (GNNs) have received increasing attention in the drug discovery domain due to their capability of integrating drugs profiles and the network structure into a low-dimensional feature space for predicting links and classification. Most of GNN models for DDI predictions are built on an unsigned graph, which tends to represent associated nodes with similar embedding results. However, semantic correlation between drugs, such as degressive effects, or even adverse side reactions should be disassortative. In this study, we put forward signed GNNs to model assortative and disassortative relationships within drug pairs. Since negative links exclude direct generalization of spectral filters on unsigned graph, we divide the signed graph into two unsigned subgraphs to dedicate two spectral filters, which captures both commonality and difference of drug pairs. For drug representations we derive two signed graph filtering-based neural networks (SGFNNs) which integrate signed graph structures and drug node attributes. Moreover, we use an end-to-end framework for learning DDIs, where an SGFNN together with a discriminator is jointly trained under a problem-specific loss function. The experimental results on two prediction problems show that our framework can obtain significant improvements compared with baselines. The case study further verifies the validation of our method.