MINDG: a drug–target interaction prediction method based on an integrated learning algorithm

• Published in: Bioinformatics (Oxford, England) Vol. 40; no. 4
• Main Authors: Yang, Hailong, Chen, Yue, Zuo, Yun, Deng, Zhaohong, Pan, Xiaoyong, Shen, Hong-Bin, Choi, Kup-Sze, Yu, Dong-Jun
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 29.03.2024; Oxford Publishing Limited (England)
• Subjects: Algorithms; Attention task; Deep learning; Drugs; Graph neural networks; Machine learning; Neural networks; Nodes; Performance evaluation; Predictions; Therapeutic targets
• ISSN: 1367-4811; 1367-4803; 1367-4811
• DOI: 10.1093/bioinformatics/btae147

Abstract Motivation Drug–target interaction (DTI) prediction refers to the prediction of whether a given drug molecule will bind to a specific target and thus exert a targeted therapeutic effect. Although intelligent computational approaches for drug target prediction have received much attention and made many advances, they are still a challenging task that requires further research. The main challenges are manifested as follows: (i) most graph neural network-based methods only consider the information of the first-order neighboring nodes (drug and target) in the graph, without learning deeper and richer structural features from the higher-order neighboring nodes. (ii) Existing methods do not consider both the sequence and structural features of drugs and targets, and each method is independent of each other, and cannot combine the advantages of sequence and structural features to improve the interactive learning effect. Results To address the above challenges, a Multi-view Integrated learning Network that integrates Deep learning and Graph Learning (MINDG) is proposed in this study, which consists of the following parts: (i) a mixed deep network is used to extract sequence features of drugs and targets, (ii) a higher-order graph attention convolutional network is proposed to better extract and capture structural features, and (iii) a multi-view adaptive integrated decision module is used to improve and complement the initial prediction results of the above two networks to enhance the prediction performance. We evaluate MINDG on two dataset and show it improved DTI prediction performance compared to state-of-the-art baselines. Availability and implementation https://github.com/jnuaipr/MINDG.