Multi-type feature fusion based on graph neural network for drug-drug interaction prediction.

(2022) 23:224 He et al. BMC Bioinformatics https://doi.org/10.1186/s12859-022-04763-2 RESEARCH BMC Bioinformatics Open Access Multi‑type feature fusion based on graph neural network for drug‑drug interaction prediction Changxiang He1, Yuru Liu1, Hao Li2, Hui Zhang3, Yaping Mao4, Xiaofei Qin2, Lele Liu1* and Xuedian Zhang2 *Correspondence: 1 College of Science, University of Shanghai for Science and Technology, Shanghai 200093, China 2 School of Optical‑Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China 3 Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China 4 School of Mathematics and Statistis, Qinghai Normal University, Xining 810008, China Abstract Background: Drug-Drug interactions (DDIs) are a challenging problem in drug research. Drug combination therapy is an effective solution to treat diseases, but it can also cause serious side effects. Therefore, DDIs prediction is critical in pharmacology. Recently, researchers have been using deep learning techniques to predict DDIs. However, these methods only consider single information of the drug and have shortcomings in robustness and scalability. Results: In this paper, we propose a multi-type feature fusion based on graph neural network model (MFFGNN) for DDI prediction, which can effectively fuse the topological information in molecular graphs, the interaction information between drugs and the local chemical context in SMILES sequences. In MFFGNN, to fully learn the topological information of drugs, we propose a novel feature extraction module to capture the global features for the molecular graph and the local features for each atom of the molecular graph. In addition, in the multi-type feature fusion module, we use the gating mechanism in each graph convolution layer to solve the over-smoothing problem during information delivery. We perform extensive experiments on multiple real datasets. The results show that MFFGNN outperforms some state-of-the-art models for DDI prediction. Moreover, the cross-dataset experiment results further show that MFFGNN has good generalization performance. Conclusions: Our proposed model can efficiently integrate the information from SMILES sequences, molecular graphs and drug-drug interaction networks. We find that a multi-type feature fusion model can accurately predict DDIs. It may contribute to discovering novel DDIs. Keywords: Multi-type feature fusion, Graph neural network, Gating mechanism, Link prediction Introduction Drug-Drug interactions (DDIs) refer to the presence of one drug changing the pharmacological activity of another, which may produce some side effects and even injury or death. At the same time, multiple drug combinations to treat diseases are inevitable. So, it is crucial to predict potential DDI. Traditional methods of DDI prediction © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publi cdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. He et al. BMC Bioinformatics (2022) 23:224 depend on in vivo and in vitro experiments. However, due to its limited environment, too small scale, cumbersome and expensive process, the ability to predicting DDI is greatly limited. Therefore, an efficient computational method is needed to predict DDI. In the past several years, people have proposed methods based on machine learning [1–4] to solve this problem. Qiu et al. [5] summarized some methods based on machine learning. Deng et al. [6] used chemical structure to learn the representation of DDIs in representation module, and then predicted some rare events with few examples in comparing module. Deng et al. [7] predicted DDI using different drug features and constructed deep neural networks (DNN). Zhang et al. [8] predicted DDI using manifold regularization. Recently, graph-based representation learning has been applied to Drug-Drug interaction. Drugs are compounds, each of which can be represented by a molecular graph with the atom as the node and the chemical bond as the edge, or a Simplified Molecular Input Line Entry System (SMILES) sequence. In Drug-Drug interaction networks, by treating the drug as the node and the interaction as the edge, DDI prediction can be regarded as link prediction tasks. Graph neural network (GNN) has made some progress in DDI prediction [9–13]. Feng et al. [14] predicted DDI using Graph Convolutional Network (GCN) and DNN. In addition, there are also many methods about multi-type DDI prediction [15–17]. Nyamabo et al. [18] proposed to predict DDIs by the interactions between drug substructures. Then, Nyamabo et al. [19] used gating devices to learn the chemical substructures of drugs. Chen et al. [20] used the bi-level cross strategy to fuse the structural information and knowledge graph information of drugs. Although the models mentioned have achieved significant results, there are still some limitations: (i) The models mentioned are generally limited to only considering the structure, sequence or interaction information of the drugs, without considering the synergistic effects between them. (ii) For molecular graphs, only applying GNN can extract the local features for the atoms of the molecular graph, but it is difficult to propagate the information in the graph remotely to capture the global features for the molecular graph. (iii) In drug-drug interaction networks, node features obtained by stacking multi-layer GNNs will be smoothed and blurred, which loses the diversity of node features. To address above issues, this paper proposes an end-to-end learning framework for DDI prediction, namely MFFGNN. In MFFGNN, we first utilize deep neural networks to capture the intra-drug features from SMILES sequences and molecular graphs. For SMILES sequences, MFFGNN applies the bi-directional gate recurrent unit neural network [21] to extract local chemical context information from the sequences. For molecular graphs, MFFGNN not only utilize (...truncated)