ALDPI: adaptively learning importance of multi-scale topologies and multi-modality similarities for drug–protein interaction prediction. Issue 2 (2nd February 2022)

• Record Type: Journal Article
• Authors: Hu, Kaimiao; Cui, Hui; Zhang, Tiangang; Sun, Chang; Xuan, Ping
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 2(2022)
• Publication Date: 2022-02-02
• Subjects: drug–protein interaction prediction -- drug—protein heterogeneous graph -- multi-scale neighbor topologies -- multi-modality similarities -- neighbor topology-level attention; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbab606
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Motivation: Effective computational methods to predict drug–protein interactions (DPIs) are vital for drug discovery in reducing the time and cost of drug development. Recent DPI prediction methods mainly exploit graph data composed of multiple kinds of connections among drugs and proteins. Each node in the graph usually has topological structures with multiple scales formed by its first-order neighbors and multi-order neighbors. However, most of the previous methods do not consider the topological structures of multi-order neighbors. In addition, deep integration of the multi-modality similarities of drugs and proteins is also a challenging task. Results: We propose a model called ALDPI to adaptively learn the multi-scale topologies and multi-modality similarities with various significance levels. We first construct a drug–protein heterogeneous graph, which is composed of the interactions and the similarities with multiple modalities among drugs and proteins. An adaptive graph learning module is then designed to learn important kinds of connections in heterogeneous graph and generate new topology graphs. A module based on graph convolutional autoencoders is established to learn multiple representations, which imply the node attributes and multiple-scale topologies composed of one-order and multi-order neighbors, respectively. We also design an attention mechanism at neighbor topology level to distinguish the importance of these representations. Finally, since each similarity modality has its specific features, we construct a multi-layer convolutional neural network-based module to learn and fuse multi-modality features to obtain the attribute representation of each drug–protein node pair. Comprehensive experimental results show ALDPI's superior performance over six state-of-the-art methods. The results of recall rates of top-ranked candidates and case studies on five drugs further demonstrate the ability of ALDPI to discover potential drug-related protein candidates. Contact: [e-mail]