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Ning Q, Wang Y, Zhao Y, Sun J, Jiang L, Wang K, Yin M. DMHGNN: Double multi-view heterogeneous graph neural network framework for drug-target interaction prediction. Artif Intell Med 2025; 159:103023. [PMID: 39579417 DOI: 10.1016/j.artmed.2024.103023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 10/09/2024] [Accepted: 11/15/2024] [Indexed: 11/25/2024]
Abstract
Accurate identification of drug-target interactions (DTIs) plays a crucial role in drug discovery. Compared with traditional experimental methods that are labor-intensive and time-consuming, computational methods for drug-target interactions prediction are more popular in recent years. Conventional computational methods almost simply view heterogeneous network constructed by the drug-related and protein-related dataset instead of comprehensively exploring drug-protein pair (DPP) information. To address this limitation, we proposed a Double Multi-view Heterogeneous Graph Neural Network framework for drug-target interaction prediction (DMHGNN). In DMHGNN, one multi-view heterogeneous graph neural network is based on meta-paths and denoising autoencoder for protein-, drug-related heterogeneous network learning, and another multi-view heterogeneous graph neural network is based on multi-channel graph convolutional network for drug-protein pair similarity network learning. First, a meta-path-based graph encoder with the attention mechanism is used for substructure learning of complex relationships from heterogeneous network constructed by proteins, drugs, side-effects and diseases, obtaining key information that is easy to be ignored in global learning of heterogeneous networks, and multi-source neighbouring features for drugs and proteins are learned from heterogeneous network via denoising auto-encoder model. Then, multi-view graphs of drug-protein pairs (DPPs) including the topology graph, semantics graph and collaborative graph with shared weights are constructed, and the multi-channel graph convolutional network (GCN) is utilized to learn the deep representation of DPPs. Finally, a multi-layer fully connection network is trained to predict drug-target interactions. Experiments have demonstrated its effectiveness and better performance than state-of-the-art methods.
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Affiliation(s)
- Qiao Ning
- The School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, Jiangsu, China; Information Science and Technology, Dalian Maritime University, Dalian 116026, Liaoning, China; Neusoft Education Technology Group, Dalian 116026, Liaoning, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130015, Jilin, China.
| | - Yue Wang
- Information Science and Technology, Dalian Maritime University, Dalian 116026, Liaoning, China
| | - Yaomiao Zhao
- Information Science and Technology, Dalian Maritime University, Dalian 116026, Liaoning, China
| | - Jiahao Sun
- Computer Science and Technology, the Northeast Normal University, Changchun 999078, Jilin, China
| | - Lu Jiang
- Information Science and Technology, Dalian Maritime University, Dalian 116026, Liaoning, China; Computer Science and Technology, the Northeast Normal University, Changchun 999078, Jilin, China.
| | - Kaidi Wang
- Computer Science and Technology, the Northeast Normal University, Changchun 999078, Jilin, China
| | - Minghao Yin
- Computer Science and Technology, the Northeast Normal University, Changchun 999078, Jilin, China.
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Li M, Liu H, Kong F, Lv P. DTRE: A model for predicting drug-target interactions of endometrial cancer based on heterogeneous graph. FUTURE GENERATION COMPUTER SYSTEMS 2024; 161:478-486. [DOI: 10.1016/j.future.2024.07.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2025]
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3
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Li Y, Zhang X, Chen Z, Yang H, Liu Y, Wang H, Yan T, Xiang J, Wang B. Accurate prediction of drug-target interactions in Chinese and western medicine by the CWI-DTI model. Sci Rep 2024; 14:25054. [PMID: 39443630 PMCID: PMC11499656 DOI: 10.1038/s41598-024-76367-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2024] [Accepted: 10/14/2024] [Indexed: 10/25/2024] Open
Abstract
Accurate prediction of drug-target interactions (DTIs) is crucial for advancing drug discovery and repurposing. Computational methods have significantly improved the efficiency of experimental predictions for drug-target interactions in Western medicine. However, accurately predicting the complex relationships between Chinese medicine ingredients and targets remains a formidable challenge due to the vast number and high heterogeneity of these ingredients. In this study, we introduce the CWI-DTI method, which achieves high-accuracy prediction of DTIs using a large dataset of interactive relationships of drug ingredients or candidate targets. Moreover, we present a novel dataset to evaluate the prediction accuracy of both Chinese and Western medicine. Through meticulous collection and preprocessing of data on ingredients and targets, we employ an innovative autoencoder framework to fuse multiple drug (target) topological similarity matrices. Additionally, we employ denoising blocks, sparse blocks, and stacked blocks to extract crucial features from the similarity matrix, reducing noise and enhancing accuracy across diverse datasets. Our results indicate that the CWI-DTI model shows improved performance compared to several existing state-of-the-art methods on the datasets tested in both Western and Chinese medicine databases. The findings of this study hold immense promise for advancing DTI prediction in Chinese and Western medicine, thus fostering more efficient drug discovery and repurposing endeavors. Our model is available at https://github.com/WANG-BIN-LAB/CWIDTI .
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Affiliation(s)
- Ying Li
- Department of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Xingyu Zhang
- Department of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Zhuo Chen
- Department of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Hongye Yang
- Department of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Yuhui Liu
- Department of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Huiqing Wang
- Department of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Ting Yan
- Department of Pathology, Shanxi Key Laboratory of Carcinogenesis and Translational Research on Esophageal Cancer, Shanxi Medical University, Taiyuan, China
| | - Jie Xiang
- Department of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Bin Wang
- Department of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China.
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Huang J, Sun C, Li M, Tang R, Xie B, Wang S, Wei JM. Structure-inclusive similarity based directed GNN: a method that can control information flow to predict drug-target binding affinity. Bioinformatics 2024; 40:btae563. [PMID: 39292540 PMCID: PMC11474107 DOI: 10.1093/bioinformatics/btae563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 05/21/2024] [Accepted: 09/17/2024] [Indexed: 09/20/2024] Open
Abstract
MOTIVATION Exploring the association between drugs and targets is essential for drug discovery and repurposing. Comparing with the traditional methods that regard the exploration as a binary classification task, predicting the drug-target binding affinity can provide more specific information. Many studies work based on the assumption that similar drugs may interact with the same target. These methods constructed a symmetric graph according to the undirected drug similarity or target similarity. Although these similarities can measure the difference between two molecules, it is unable to analyze the inclusion relationship of their substructure. For example, if drug A contains all the substructures of drug B, then in the message-passing mechanism of the graph neural network, drug A should acquire all the properties of drug B, while drug B should only obtain some of the properties of A. RESULTS To this end, we proposed a structure-inclusive similarity (SIS) which measures the similarity of two drugs by considering the inclusion relationship of their substructures. Based on SIS, we constructed a drug graph and a target graph, respectively, and predicted the binding affinities between drugs and targets by a graph convolutional network-based model. Experimental results show that considering the inclusion relationship of the substructure of two molecules can effectively improve the accuracy of the prediction model. The performance of our SIS-based prediction method outperforms several state-of-the-art methods for drug-target binding affinity prediction. The case studies demonstrate that our model is a practical tool to predict the binding affinity between drugs and targets. AVAILABILITY AND IMPLEMENTATION Source codes and data are available at https://github.com/HuangStomach/SISDTA.
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Affiliation(s)
- Jipeng Huang
- Centre for Bioinformatics and Intelligent Medicine, Nankai University, Tianjin 300071, China
- College of Computer Science, Nankai University, Tianjin 300071, China
- Tianjin Key Laboratory of Network and Data Security, Tianjin 300350, China
| | - Chang Sun
- Centre for Bioinformatics and Intelligent Medicine, Nankai University, Tianjin 300071, China
- College of Computer Science, Nankai University, Tianjin 300071, China
- Tianjin Key Laboratory of Network and Data Security, Tianjin 300350, China
| | - Minglei Li
- Centre for Bioinformatics and Intelligent Medicine, Nankai University, Tianjin 300071, China
- College of Computer Science, Nankai University, Tianjin 300071, China
- Tianjin Key Laboratory of Network and Data Security, Tianjin 300350, China
| | - Rong Tang
- Centre for Bioinformatics and Intelligent Medicine, Nankai University, Tianjin 300071, China
- College of Computer Science, Nankai University, Tianjin 300071, China
- Tianjin Key Laboratory of Network and Data Security, Tianjin 300350, China
| | - Bin Xie
- College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang 050024, China
| | - Shuqin Wang
- College of Computer and Information Engineering, Tianjin Normal University, Tianjin, Xi Qing District 300387, China
| | - Jin-Mao Wei
- Centre for Bioinformatics and Intelligent Medicine, Nankai University, Tianjin 300071, China
- College of Computer Science, Nankai University, Tianjin 300071, China
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Wu H, Liu J, Zhang R, Lu Y, Cui G, Cui Z, Ding Y. A review of deep learning methods for ligand based drug virtual screening. FUNDAMENTAL RESEARCH 2024; 4:715-737. [PMID: 39156568 PMCID: PMC11330120 DOI: 10.1016/j.fmre.2024.02.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 01/10/2024] [Accepted: 02/18/2024] [Indexed: 08/20/2024] Open
Abstract
Drug discovery is costly and time consuming, and modern drug discovery endeavors are progressively reliant on computational methodologies, aiming to mitigate temporal and financial expenditures associated with the process. In particular, the time required for vaccine and drug discovery is prolonged during emergency situations such as the coronavirus 2019 pandemic. Recently, the performance of deep learning methods in drug virtual screening has been particularly prominent. It has become a concern for researchers how to summarize the existing deep learning in drug virtual screening, select different models for different drug screening problems, exploit the advantages of deep learning models, and further improve the capability of deep learning in drug virtual screening. This review first introduces the basic concepts of drug virtual screening, common datasets, and data representation methods. Then, large numbers of common deep learning methods for drug virtual screening are compared and analyzed. In addition, a dataset of different sizes is constructed independently to evaluate the performance of each deep learning model for the difficult problem of large-scale ligand virtual screening. Finally, the existing challenges and future directions in the field of virtual screening are presented.
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Affiliation(s)
- Hongjie Wu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Junkai Liu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Runhua Zhang
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Yaoyao Lu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Guozeng Cui
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Zhiming Cui
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Yijie Ding
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China
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Wei J, Lu L, Shen T. Predicting drug-protein interactions by preserving the graph information of multi source data. BMC Bioinformatics 2024; 25:10. [PMID: 38177981 PMCID: PMC10768380 DOI: 10.1186/s12859-023-05620-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 12/15/2023] [Indexed: 01/06/2024] Open
Abstract
Examining potential drug-target interactions (DTIs) is a pivotal component of drug discovery and repurposing. Recently, there has been a significant rise in the use of computational techniques to predict DTIs. Nevertheless, previous investigations have predominantly concentrated on assessing either the connections between nodes or the consistency of the network's topological structure in isolation. Such one-sided approaches could severely hinder the accuracy of DTI predictions. In this study, we propose a novel method called TTGCN, which combines heterogeneous graph convolutional neural networks (GCN) and graph attention networks (GAT) to address the task of DTI prediction. TTGCN employs a two-tiered feature learning strategy, utilizing GAT and residual GCN (R-GCN) to extract drug and target embeddings from the diverse network, respectively. These drug and target embeddings are then fused through a mean-pooling layer. Finally, we employ an inductive matrix completion technique to forecast DTIs while preserving the network's node connectivity and topological structure. Our approach demonstrates superior performance in terms of area under the curve and area under the precision-recall curve in experimental comparisons, highlighting its significant advantages in predicting DTIs. Furthermore, case studies provide additional evidence of its ability to identify potential DTIs.
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Affiliation(s)
- Jiahao Wei
- School of Mathematical Sciences, Guizhou Normal University, Guiyang, 550025, China
| | - Linzhang Lu
- School of Mathematical Sciences, Guizhou Normal University, Guiyang, 550025, China.
- School of Mathematical Sciences, Xiamen University, Xiamen, 361005, China.
| | - Tie Shen
- Key Laboratory of Information and Computing Science Guizhou Province, Guizhou Normal University, Guizhou, 550001, China.
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Li H, Wang S, Zheng W, Yu L. Multi-dimensional search for drug-target interaction prediction by preserving the consistency of attention distribution. Comput Biol Chem 2023; 107:107968. [PMID: 37844375 DOI: 10.1016/j.compbiolchem.2023.107968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 09/27/2023] [Accepted: 10/05/2023] [Indexed: 10/18/2023]
Abstract
Predicting drug-target interaction (DTI) is a crucial step in the process of drug repurposing and new drug development. Although the attention mechanism has been widely used to capture the interactions between drugs and targets, it mainly uses the Simplified Molecular Input Line Entry System (SMILES) and two-dimensional (2D) molecular graph features of drugs. In this paper, we propose a neural network model called MdDTI for DTI prediction. The model searches for binding sites that may interact with the target from the multiple dimensions of drug structure, namely the 2D substructures and the three-dimensional (3D) spatial structure. For the 2D substructures, we have developed a novel substructure decomposition strategy based on drug molecular graphs and compared its performance with the SMILES-based decomposition method. For the 3D spatial structure of drugs, we constructed spatial feature representation matrices for drugs based on the Cartesian coordinates of heavy atoms (without hydrogen atoms) in each drug. Finally, to ensure the search results of the model are consistent across multiple dimensions, we construct a consistency loss function. We evaluate MdDTI on four drug-target interaction datasets and three independent compound-protein affinity test sets. The results indicate that our model surpasses a series of state-of-the-art models. Case studies demonstrate that our model is capable of capturing the potential binding regions between drugs and targets, and it shows efficacy in drug repurposing. Our code is available at https://github.com/lhhu1999/MdDTI.
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Affiliation(s)
- Huaihu Li
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650504, Yunnan, China
| | - Shunfang Wang
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650504, Yunnan, China; The Key Lab of Intelligent Systems and Computing of Yunnan Province, Yunnan University, Kunming, Yunnan, China.
| | - Weihua Zheng
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650504, Yunnan, China
| | - Li Yu
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650504, Yunnan, China
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Wang Y, Zhang Z, Piao C, Huang Y, Zhang Y, Zhang C, Lu YJ, Liu D. LDS-CNN: a deep learning framework for drug-target interactions prediction based on large-scale drug screening. Health Inf Sci Syst 2023; 11:42. [PMID: 37667773 PMCID: PMC10475000 DOI: 10.1007/s13755-023-00243-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 08/14/2023] [Indexed: 09/06/2023] Open
Abstract
Background Drug-target interaction (DTI) is a vital drug design strategy that plays a significant role in many processes of complex diseases and cellular events. In the face of challenges such as extensive protein data and experimental costs, it is suggested to apply bioinformatics approaches to exploit potential interactions to design new targeted medications. Different data and interaction types bring difficulties to study involving incompatible and heterology formats. The analysis of drug-target interactions in a comprehensive and unified model is a significant challenge. Method Here, we propose a general method for predicting interactions between small-molecule drugs and protein targets, Large-scale Drug target Screening Convolutional Neural Network (LDS-CNN), which used unified encoding to achieve the calculation of the different data formats in an integrated model to realize feature abstraction and potential object prediction. Result On 898,412 interaction data involving 1683 small-molecule compounds and 14,350 human proteins from 8.8 billion records, the proposed method achieved an area under the curve (AUC) of 0.96, an area under the precision-recall curve (AUPRC) of 0.95, and an accuracy of 90.13%. The experimental results illustrated that the proposed method attained high accuracy on the test set, indicating its high predictive ability in drug-target interaction prediction. LDS-CNN is effective for the prediction of large-scale datasets and datasets composed of data with different formats. Conclusion In this study, we propose a DTI prediction method to solve the problems of unified encoding of large-scale data in multiple formats. It provides a feasible way to efficiently abstract the features among different types of drug-related data, thus reducing experimental costs and time consumption. The proposed method can be used to identify potential drug targets and candidates for the treatment of complex diseases. This work provides a reference for DTI to process large-scale data and different formats with deep learning methods and provides certain suggestions for future research.
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Affiliation(s)
- Yang Wang
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006 China
| | - Zuxian Zhang
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou, 510006 China
| | - Chenghong Piao
- The First Affiliated Hospital of Ningbo University, Ningbo, 315010 China
| | - Ying Huang
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou, 510006 China
| | - Yihan Zhang
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou, 510006 China
| | - Chi Zhang
- Shanghai Institute of Biological Products, Shanghai, 201403 China
| | - Yu-Jing Lu
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou, 510006 China
- Smart Medical Innovation Technology Center, Guangdong University of Technology, Guangzhou, 510006 China
| | - Dongning Liu
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006 China
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Su Y, Hu Z, Wang F, Bin Y, Zheng C, Li H, Chen H, Zeng X. AMGDTI: drug-target interaction prediction based on adaptive meta-graph learning in heterogeneous network. Brief Bioinform 2023; 25:bbad474. [PMID: 38145949 PMCID: PMC10749791 DOI: 10.1093/bib/bbad474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 11/10/2023] [Accepted: 11/30/2023] [Indexed: 12/27/2023] Open
Abstract
Prediction of drug-target interactions (DTIs) is essential in medicine field, since it benefits the identification of molecular structures potentially interacting with drugs and facilitates the discovery and reposition of drugs. Recently, much attention has been attracted to network representation learning to learn rich information from heterogeneous data. Although network representation learning algorithms have achieved success in predicting DTI, several manually designed meta-graphs limit the capability of extracting complex semantic information. To address the problem, we introduce an adaptive meta-graph-based method, termed AMGDTI, for DTI prediction. In the proposed AMGDTI, the semantic information is automatically aggregated from a heterogeneous network by training an adaptive meta-graph, thereby achieving efficient information integration without requiring domain knowledge. The effectiveness of the proposed AMGDTI is verified on two benchmark datasets. Experimental results demonstrate that the AMGDTI method overall outperforms eight state-of-the-art methods in predicting DTI and achieves the accurate identification of novel DTIs. It is also verified that the adaptive meta-graph exhibits flexibility and effectively captures complex fine-grained semantic information, enabling the learning of intricate heterogeneous network topology and the inference of potential drug-target relationship.
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Affiliation(s)
- Yansen Su
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, 230601, China
| | - Zhiyang Hu
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, 230601, China
| | - Fei Wang
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, 230601, China
| | - Yannan Bin
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, 230601, China
| | - Chunhou Zheng
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, 230601, China
| | - Haitao Li
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, 230601, China
| | - Haowen Chen
- College of Computer Science and Electronic Engineering, Hunan University, Hunan, 410082, China
| | - Xiangxiang Zeng
- College of Computer Science and Electronic Engineering, Hunan University, Hunan, 410082, China
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Tang R, Sun C, Huang J, Li M, Wei J, Liu J. Predicting Drug-Protein Interactions by Self-Adaptively Adjusting the Topological Structure of the Heterogeneous Network. IEEE J Biomed Health Inform 2023; 27:5675-5684. [PMID: 37672364 DOI: 10.1109/jbhi.2023.3312374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
Many powerful computational methods based on graph neural networks (GNNs) have been proposed to predict drug-protein interactions (DPIs). It can effectively reduce laboratory workload and the cost of drug discovery and drug repurposing. However, many clinical functions of drugs and proteins are unknown due to their unobserved indications. Therefore, it is difficult to establish a reliable drug-protein heterogeneous network that can describe the relationships between drugs and proteins based on the available information. To solve this problem, we propose a DPI prediction method that can self-adaptively adjust the topological structure of the heterogeneous networks, and name it SATS. SATS establishes a representation learning module based on graph attention network to carry out the drug-protein heterogeneous network. It can self-adaptively learn the relationships among the nodes based on their attributes and adjust the topological structure of the network according to the training loss of the model. Finally, SATS predicts the interaction propensity between drugs and proteins based on their embeddings. The experimental results show that SATS can effectively improve the topological structure of the network. The performance of SATS outperforms several state-of-the-art DPI prediction methods under various evaluation metrics. These prove that SATS is useful to deal with incomplete data and unreliable networks. The case studies on the top section of the prediction results further demonstrate that SATS is powerful for discovering novel DPIs.
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Zhang Y, Feng Y, Wu M, Deng Z, Wang S. VGAEDTI: drug-target interaction prediction based on variational inference and graph autoencoder. BMC Bioinformatics 2023; 24:278. [PMID: 37415176 DOI: 10.1186/s12859-023-05387-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 06/16/2023] [Indexed: 07/08/2023] Open
Abstract
MOTIVATION Accurate identification of Drug-Target Interactions (DTIs) plays a crucial role in many stages of drug development and drug repurposing. (i) Traditional methods do not consider the use of multi-source data and do not consider the complex relationship between data sources. (ii) How to better mine the hidden features of drug and target space from high-dimensional data, and better solve the accuracy and robustness of the model. RESULTS To solve the above problems, a novel prediction model named VGAEDTI is proposed in this paper. We constructed a heterogeneous network with multiple sources of information using multiple types of drug and target dataIn order to obtain deeper features of drugs and targets, we use two different autoencoders. One is variational graph autoencoder (VGAE) which is used to infer feature representations from drug and target spaces. The second is graph autoencoder (GAE) propagating labels between known DTIs. Experimental results on two public datasets show that the prediction accuracy of VGAEDTI is better than that of six DTIs prediction methods. These results indicate that model can predict new DTIs and provide an effective tool for accelerating drug development and repurposing.
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Affiliation(s)
- Yuanyuan Zhang
- Yinfei Feng Qingdao University of Technology, Qingdao, China
| | - Yinfei Feng
- Yinfei Feng Qingdao University of Technology, Qingdao, China.
| | - Mengjie Wu
- Yinfei Feng Qingdao University of Technology, Qingdao, China
| | - Zengqian Deng
- Yinfei Feng Qingdao University of Technology, Qingdao, China
| | - Shudong Wang
- School of Computer Science and Technology, China University of Petroleum, Qingdao, China
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Chen ZH, Zhao BW, Li JQ, Guo ZH, You ZH. GraphCPIs: A novel graph-based computational model for potential compound-protein interactions. MOLECULAR THERAPY. NUCLEIC ACIDS 2023; 32:721-728. [PMID: 37251691 PMCID: PMC10209012 DOI: 10.1016/j.omtn.2023.04.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 04/28/2023] [Indexed: 05/31/2023]
Abstract
Identifying proteins that interact with drug compounds has been recognized as an important part in the process of drug discovery. Despite extensive efforts that have been invested in predicting compound-protein interactions (CPIs), existing traditional methods still face several challenges. The computer-aided methods can identify high-quality CPI candidates instantaneously. In this research, a novel model is named GraphCPIs, proposed to improve the CPI prediction accuracy. First, we establish the adjacent matrix of entities connected to both drugs and proteins from the collected dataset. Then, the feature representation of nodes could be obtained by using the graph convolutional network and Grarep embedding model. Finally, an extreme gradient boosting (XGBoost) classifier is exploited to identify potential CPIs based on the stacked two kinds of features. The results demonstrate that GraphCPIs achieves the best performance, whose average predictive accuracy rate reaches 90.09%, average area under the receiver operating characteristic curve is 0.9572, and the average area under the precision and recall curve is 0.9621. Moreover, comparative experiments reveal that our method surpasses the state-of-the-art approaches in the field of accuracy and other indicators with the same experimental environment. We believe that the GraphCPIs model will provide valuable insight to discover novel candidate drug-related proteins.
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Affiliation(s)
- Zhan-Heng Chen
- Department of Clinical Anesthesiology, Faculty of Anesthesiology, Naval Medical University, Shanghai 200433, China
| | - Bo-Wei Zhao
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
| | - Jian-Qiang Li
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
| | - Zhen-Hao Guo
- Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Caoan Road 4800, Shanghai 201804, China
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
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Abbasi Mesrabadi H, Faez K, Pirgazi J. Drug-target interaction prediction based on protein features, using wrapper feature selection. Sci Rep 2023; 13:3594. [PMID: 36869062 PMCID: PMC9984486 DOI: 10.1038/s41598-023-30026-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Accepted: 02/14/2023] [Indexed: 03/05/2023] Open
Abstract
Drug-target interaction prediction is a vital stage in drug development, involving lots of methods. Experimental methods that identify these relationships on the basis of clinical remedies are time-taking, costly, laborious, and complex introducing a lot of challenges. One group of new methods is called computational methods. The development of new computational methods which are more accurate can be preferable to experimental methods, in terms of total cost and time. In this paper, a new computational model to predict drug-target interaction (DTI), consisting of three phases, including feature extraction, feature selection, and classification is proposed. In feature extraction phase, different features such as EAAC, PSSM and etc. would be extracted from sequence of proteins and fingerprint features from drugs. These extracted features would then be combined. In the next step, one of the wrapper feature selection methods named IWSSR, due to the large amount of extracted data, is applied. The selected features are then given to rotation forest classification, to have a more efficient prediction. Actually, the innovation of our work is that we extract different features; and then select features by the use of IWSSR. The accuracy of the rotation forest classifier based on tenfold on the golden standard datasets (enzyme, ion channels, G-protein-coupled receptors, nuclear receptors) is as follows: 98.12, 98.07, 96.82, and 95.64. The results of experiments indicate that the proposed model has an acceptable rate in DTI prediction and is compatible with the proposed methods in other papers.
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Affiliation(s)
- Hengame Abbasi Mesrabadi
- Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
| | - Karim Faez
- Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.
| | - Jamshid Pirgazi
- Department of Computer Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran
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14
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Li Y, Sun C, Wei JM, Liu J. Drug-Protein interaction prediction by correcting the effect of incomplete information in heterogeneous information. Bioinformatics 2022; 38:5073-5080. [PMID: 36111859 DOI: 10.1093/bioinformatics/btac629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 08/30/2022] [Accepted: 09/15/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Large-scale heterogeneous data provide diverse perspectives for predicting drug-protein interactions (DPIs). However, the available information on molecular interactions and clinical associations related to drugs or proteins is incomplete because there may be unproven interactions and associations. This incomplete information in the available data is presented in the form of non-interaction and non-correlation, which may mislead the prediction model. Existing methods fuse incomplete and complete information without considering their integrity, so the negative effects of incomplete information still exist. RESULTS We develop a network-based DPI prediction method named BRWCP, which uses the complete information network to correct the prediction results acquired by the incomplete information network. By integrating relevant heterogeneous information that may be incomplete, the feature similarities of drugs and proteins are obtained. Combining the feature similarities and known DPIs, an incomplete information-based drug-protein heterogeneous network is constructed. Then, a bidirectional random walk with pruning algorithm is adopted in this heterogeneous network to predict potential DPIs. Next, the predicted DPIs are combined with the chemical fingerprint similarity of drugs and amino acid sequence similarity of proteins to construct the complete information network. The bidirectional random walk with pruning algorithm is applied in the new network to obtain the final prediction results until it converges. Experimental results show that BRWCP is superior to several state-of-the-art DPI prediction methods, and case studies further confirm its ability to tap potential DPIs. AVAILABILITY AND IMPLEMENTATION The code and data used in BRWCP are available at https://github.com/lyfdomain/BRWCP. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yanfei Li
- College of Computer Science, Nankai University, Tianjin 300071, China.,Institute of Big Data, Nankai University, Tianjin 300071, China
| | - Chang Sun
- College of Computer Science, Nankai University, Tianjin 300071, China.,Institute of Big Data, Nankai University, Tianjin 300071, China
| | - Jin-Mao Wei
- College of Computer Science, Nankai University, Tianjin 300071, China.,Institute of Big Data, Nankai University, Tianjin 300071, China
| | - Jian Liu
- College of Computer Science, Nankai University, Tianjin 300071, China.,Institute of Big Data, Nankai University, Tianjin 300071, China
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15
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Wang H, Guo F, Du M, Wang G, Cao C. A novel method for drug-target interaction prediction based on graph transformers model. BMC Bioinformatics 2022; 23:459. [PMID: 36329406 PMCID: PMC9635108 DOI: 10.1186/s12859-022-04812-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 06/23/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Drug-target interactions (DTIs) prediction becomes more and more important for accelerating drug research and drug repositioning. Drug-target interaction network is a typical model for DTIs prediction. As many different types of relationships exist between drug and target, drug-target interaction network can be used for modeling drug-target interaction relationship. Recent works on drug-target interaction network are mostly concentrate on drug node or target node and neglecting the relationships between drug-target. RESULTS We propose a novel prediction method for modeling the relationship between drug and target independently. Firstly, we use different level relationships of drugs and targets to construct feature of drug-target interaction. Then, we use line graph to model drug-target interaction. After that, we introduce graph transformer network to predict drug-target interaction. CONCLUSIONS This method introduces a line graph to model the relationship between drug and target. After transforming drug-target interactions from links to nodes, a graph transformer network is used to accomplish the task of predicting drug-target interactions.
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Affiliation(s)
- Hongmei Wang
- College of Computer Science and Engineering, Changchun University of Technology, Changchun, China
| | - Fang Guo
- College of Computer Science and Engineering, Changchun University of Technology, Changchun, China
| | - Mengyan Du
- College of Computer Science and Engineering, Changchun University of Technology, Changchun, China
| | - Guishen Wang
- College of Computer Science and Engineering, Changchun University of Technology, Changchun, China.
| | - Chen Cao
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China. .,Department of Biochemistry and Molecular Biology, Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Canada.
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16
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Wan X, Wu X, Wang D, Tan X, Liu X, Fu Z, Jiang H, Zheng M, Li X. An inductive graph neural network model for compound-protein interaction prediction based on a homogeneous graph. Brief Bioinform 2022; 23:bbac073. [PMID: 35275993 PMCID: PMC9310259 DOI: 10.1093/bib/bbac073] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 02/09/2022] [Accepted: 02/11/2022] [Indexed: 01/10/2023] Open
Abstract
Identifying the potential compound-protein interactions (CPIs) plays an essential role in drug development. The computational approaches for CPI prediction can reduce time and costs of experimental methods and have benefited from the continuously improved graph representation learning. However, most of the network-based methods use heterogeneous graphs, which is challenging due to their complex structures and heterogeneous attributes. Therefore, in this work, we transformed the compound-protein heterogeneous graph to a homogeneous graph by integrating the ligand-based protein representations and overall similarity associations. We then proposed an Inductive Graph AggrEgator-based framework, named CPI-IGAE, for CPI prediction. CPI-IGAE learns the low-dimensional representations of compounds and proteins from the homogeneous graph in an end-to-end manner. The results show that CPI-IGAE performs better than some state-of-the-art methods. Further ablation study and visualization of embeddings reveal the advantages of the model architecture and its role in feature extraction, and some of the top ranked CPIs by CPI-IGAE have been validated by a review of recent literature. The data and source codes are available at https://github.com/wanxiaozhe/CPI-IGAE.
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Affiliation(s)
- Xiaozhe Wan
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing 100049, China
| | - Xiaolong Wu
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Dingyan Wang
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing 100049, China
| | | | - Xiaohong Liu
- AlphaMa Inc., No. 108, Yuxin Road, Suzhou Industrial Park, Suzhou 215128, China
| | - Zunyun Fu
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
| | - Hualiang Jiang
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China; School of Life Science and Technology, ShanghaiTech University, 393 Huaxiazhong Road, Shanghai 200031, China
| | - Mingyue Zheng
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
| | - Xutong Li
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
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Xuan P, Fan M, Cui H, Zhang T, Nakaguchi T. GVDTI: graph convolutional and variational autoencoders with attribute-level attention for drug-protein interaction prediction. Brief Bioinform 2021; 23:6412398. [PMID: 34718408 DOI: 10.1093/bib/bbab453] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 09/14/2021] [Accepted: 10/02/2021] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Identifying proteins that interact with drugs plays an important role in the initial period of developing drugs, which helps to reduce the development cost and time. Recent methods for predicting drug-protein interactions mainly focus on exploiting various data about drugs and proteins. These methods failed to completely learn and integrate the attribute information of a pair of drug and protein nodes and their attribute distribution. RESULTS We present a new prediction method, GVDTI, to encode multiple pairwise representations, including attention-enhanced topological representation, attribute representation and attribute distribution. First, a framework based on graph convolutional autoencoder is constructed to learn attention-enhanced topological embedding that integrates the topology structure of a drug-protein network for each drug and protein nodes. The topological embeddings of each drug and each protein are then combined and fused by multi-layer convolution neural networks to obtain the pairwise topological representation, which reveals the hidden topological relationships between drug and protein nodes. The proposed attribute-wise attention mechanism learns and adjusts the importance of individual attribute in each topological embedding of drug and protein nodes. Secondly, a tri-layer heterogeneous network composed of drug, protein and disease nodes is created to associate the similarities, interactions and associations across the heterogeneous nodes. The attribute distribution of the drug-protein node pair is encoded by a variational autoencoder. The pairwise attribute representation is learned via a multi-layer convolutional neural network to deeply integrate the attributes of drug and protein nodes. Finally, the three pairwise representations are fused by convolutional and fully connected neural networks for drug-protein interaction prediction. The experimental results show that GVDTI outperformed other seven state-of-the-art methods in comparison. The improved recall rates indicate that GVDTI retrieved more actual drug-protein interactions in the top ranked candidates than conventional methods. Case studies on five drugs further confirm GVDTI's ability in discovering the potential candidate drug-related proteins. CONTACT zhang@hlju.edu.cn Supplementary information: Supplementary data are available at Briefings in Bioinformatics online.
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Affiliation(s)
- Ping Xuan
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
| | - Mengsi Fan
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
| | - Hui Cui
- Department of Computer Science and Information Technology, La Trobe University, Melbourne 3083, Australia
| | - Tiangang Zhang
- School of Mathematical Science, Heilongjiang University, Harbin 150080, China
| | - Toshiya Nakaguchi
- Center for Frontier Medical Engineering, Chiba University, Chiba 2638522, Japan
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Xuan P, Hu K, Cui H, Zhang T, Nakaguchi T. Learning multi-scale heterogeneous representations and global topology for drug-target interaction prediction. IEEE J Biomed Health Inform 2021; 26:1891-1902. [PMID: 34673498 DOI: 10.1109/jbhi.2021.3121798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Identification of drug-target interactions (DTIs) plays a critical role in drug discovery and repositioning. Deep integration of inter-connections and intra-similarities between heterogeneous multi-source data related to drugs and targets, however, is a challenging issue. We propose a DTI prediction model by learning from drug and protein related multi-scale attributes and global topology formed by heterogeneous connections. A drug-protein-disease heterogeneous network (RPD-Net) is firstly constructed to associate diverse similarities, interactions and associations across nodes. Secondly, we propose a multi-scale pairwise deep representation learning module consisting of a new embedding strategy to integrate diverse inter-relations and intra-relations, and dilation convolutions for multi-scale deep representation extraction. A global topology learning module is proposed which is composed of strategy based on non-negative matrix factorization (NMF) to extract topology from RPD-Net, and a new relational-level attention mechanism for discriminative topology embedding. Experimental results using public dataset demonstrate improved performance over state-of-the-art methods and contributions of our major innovations. Evaluation results by top k recall rates and case studies on five drugs further show the effectiveness in retrieving potential target candidates for drugs.
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