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Zhang B, Niu D, Zhang L, Zhang Q, Li Z. MSH-DTI: multi-graph convolution with self-supervised embedding and heterogeneous aggregation for drug-target interaction prediction. BMC Bioinformatics 2024; 25:275. [PMID: 39179993 PMCID: PMC11342675 DOI: 10.1186/s12859-024-05904-5] [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: 05/17/2024] [Accepted: 08/16/2024] [Indexed: 08/26/2024] Open
Abstract
BACKGROUND The rise of network pharmacology has led to the widespread use of network-based computational methods in predicting drug target interaction (DTI). However, existing DTI prediction models typically rely on a limited amount of data to extract drug and target features, potentially affecting the comprehensiveness and robustness of features. In addition, although multiple networks are used for DTI prediction, the integration of heterogeneous information often involves simplistic aggregation and attention mechanisms, which may impose certain limitations. RESULTS MSH-DTI, a deep learning model for predicting drug-target interactions, is proposed in this paper. The model uses self-supervised learning methods to obtain drug and target structure features. A Heterogeneous Interaction-enhanced Feature Fusion Module is designed for multi-graph construction, and the graph convolutional networks are used to extract node features. With the help of an attention mechanism, the model focuses on the important parts of different features for prediction. Experimental results show that the AUROC and AUPR of MSH-DTI are 0.9620 and 0.9605 respectively, outperforming other models on the DTINet dataset. CONCLUSION The proposed MSH-DTI is a helpful tool to discover drug-target interactions, which is also validated through case studies in predicting new DTIs.
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Affiliation(s)
- Beiyi Zhang
- College of Computer Science and Technology, Qingdao University, Ningxia Road, Qingdao, 266071, Shandong, China
| | - Dongjiang Niu
- College of Computer Science and Technology, Qingdao University, Ningxia Road, Qingdao, 266071, Shandong, China
| | - Lianwei Zhang
- College of Computer Science and Technology, Qingdao University, Ningxia Road, Qingdao, 266071, Shandong, China
| | - Qiang Zhang
- College of Computer Science and Technology, Qingdao University, Ningxia Road, Qingdao, 266071, Shandong, China
| | - Zhen Li
- College of Computer Science and Technology, Qingdao University, Ningxia Road, Qingdao, 266071, Shandong, China.
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Song W, Xu L, Han C, Tian Z, Zou Q. Drug-target interaction predictions with multi-view similarity network fusion strategy and deep interactive attention mechanism. Bioinformatics 2024; 40:btae346. [PMID: 38837345 PMCID: PMC11164831 DOI: 10.1093/bioinformatics/btae346] [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: 12/22/2023] [Revised: 05/06/2024] [Accepted: 05/28/2024] [Indexed: 06/07/2024] Open
Abstract
MOTIVATION Accurately identifying the drug-target interactions (DTIs) is one of the crucial steps in the drug discovery and drug repositioning process. Currently, many computational-based models have already been proposed for DTI prediction and achieved some significant improvement. However, these approaches pay little attention to fuse the multi-view similarity networks related to drugs and targets in an appropriate way. Besides, how to fully incorporate the known interaction relationships to accurately represent drugs and targets is not well investigated. Therefore, there is still a need to improve the accuracy of DTI prediction models. RESULTS In this study, we propose a novel approach that employs Multi-view similarity network fusion strategy and deep Interactive attention mechanism to predict Drug-Target Interactions (MIDTI). First, MIDTI constructs multi-view similarity networks of drugs and targets with their diverse information and integrates these similarity networks effectively in an unsupervised manner. Then, MIDTI obtains the embeddings of drugs and targets from multi-type networks simultaneously. After that, MIDTI adopts the deep interactive attention mechanism to further learn their discriminative embeddings comprehensively with the known DTI relationships. Finally, we feed the learned representations of drugs and targets to the multilayer perceptron model and predict the underlying interactions. Extensive results indicate that MIDTI significantly outperforms other baseline methods on the DTI prediction task. The results of the ablation experiments also confirm the effectiveness of the attention mechanism in the multi-view similarity network fusion strategy and the deep interactive attention mechanism. AVAILABILITY AND IMPLEMENTATION https://github.com/XuLew/MIDTI.
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Affiliation(s)
- Wei Song
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China
| | - Lewen Xu
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China
| | - Chenguang Han
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China
| | - Zhen Tian
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China
| | - Quan Zou
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China
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Chen S, Li M, Semenov I. MFA-DTI: Drug-target interaction prediction based on multi-feature fusion adopted framework. Methods 2024; 224:79-92. [PMID: 38430967 DOI: 10.1016/j.ymeth.2024.02.008] [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: 12/31/2023] [Revised: 02/16/2024] [Accepted: 02/23/2024] [Indexed: 03/05/2024] Open
Abstract
The identification of drug-target interactions (DTI) is a valuable step in the drug discovery and repositioning process. However, traditional laboratory experiments are time-consuming and expensive. Computational methods have streamlined research to determine DTIs. The application of deep learning methods has significantly improved the prediction performance for DTIs. Modern deep learning methods can leverage multiple sources of information, including sequence data that contains biological structural information, and interaction data. While useful, these methods cannot be effectively applied to each type of information individually (e.g., chemical structure and interaction network) and do not take into account the specificity of DTI data such as low- or zero-interaction biological entities. To overcome these limitations, we propose a method called MFA-DTI (Multi-feature Fusion Adopted framework for DTI). MFA-DTI consists of three modules: an interaction graph learning module that processes the interaction network to generate interaction vectors, a chemical structure learning module that extracts features from the chemical structure, and a fusion module that combines these features for the final prediction. To validate the performance of MFA-DTI, we conducted experiments on six public datasets under different settings. The results indicate that the proposed method is highly effective in various settings and outperforms state-of-the-art methods.
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Affiliation(s)
- Siqi Chen
- School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing, 400074, China.
| | - Minghui Li
- Beidahuang Industry Group General Hospital, Harbin, 150006, China
| | - Ivan Semenov
- College of Intelligence and Computing, Tianjin University, Tianjin, 300072, China
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E Z, Qiao G, Wang G, Li Y. GSL-DTI: Graph structure learning network for Drug-Target interaction prediction. Methods 2024; 223:136-145. [PMID: 38360082 DOI: 10.1016/j.ymeth.2024.01.018] [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: 07/22/2023] [Revised: 12/23/2023] [Accepted: 01/29/2024] [Indexed: 02/17/2024] Open
Abstract
MOTIVATION Drug-target interaction prediction is an important area of research to predict whether there is an interaction between a drug molecule and its target protein. It plays a critical role in drug discovery and development by facilitating the identification of potential drug candidates and expediting the overall process. Given the time-consuming, expensive, and high-risk nature of traditional drug discovery methods, the prediction of drug-target interactions has become an indispensable tool. Using machine learning and deep learning to tackle this class of problems has become a mainstream approach, and graph-based models have recently received much attention in this field. However, many current graph-based Drug-Target Interaction (DTI) prediction methods rely on manually defined rules to construct the Drug-Protein Pair (DPP) network during the DPP representation learning process. However, these methods fail to capture the true underlying relationships between drug molecules and target proteins. RESULTS We propose GSL-DTI, an automatic graph structure learning model used for predicting drug-target interactions (DTIs). Initially, we integrate large-scale heterogeneous networks using a graph convolution network based on meta-paths, effectively learning the representations of drugs and target proteins. Subsequently, we construct drug-protein pairs based on these representations. In contrast to previous studies that construct DPP networks based on manual rules, our method introduces an automatic graph structure learning approach. This approach utilizes a filter gate on the affinity scores of DPPs and relies on the classification loss of downstream tasks to guide the learning of the underlying DPP network structure. Based on the learned DPP network, we transform the prediction of drug-target interactions into a node classification problem. The comprehensive experiments conducted on three public datasets have shown the superiority of GSL-DTI in the tasks of DTI prediction. Additionally, GSL-DTI provides a fresh perspective for advancing research in graph structure learning for DTI prediction.
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Affiliation(s)
- Zixuan E
- College of Computer and Control Engineering, Northeast Forestry University,Harbin 150006, China
| | - Guanyu Qiao
- College of Computer and Control Engineering, Northeast Forestry University,Harbin 150006, China
| | - Guohua Wang
- College of Computer and Control Engineering, Northeast Forestry University,Harbin 150006, China.
| | - Yang Li
- College of Computer and Control Engineering, Northeast Forestry University,Harbin 150006, China.
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Arnold A, Alexander J, Liu G, Stokes JM. Applications of machine learning in microbial natural product drug discovery. Expert Opin Drug Discov 2023; 18:1259-1272. [PMID: 37651150 DOI: 10.1080/17460441.2023.2251400] [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: 06/08/2023] [Accepted: 08/21/2023] [Indexed: 09/01/2023]
Abstract
INTRODUCTION Natural products (NPs) are a desirable source of new therapeutics due to their structural diversity and evolutionarily optimized bioactivities. NPs and their derivatives account for roughly 70% of approved pharmaceuticals. However, the rate at which novel NPs are discovered has decreased. To accelerate the microbial NP discovery process, machine learning (ML) is being applied to numerous areas of NP discovery and development. AREAS COVERED This review explores the utility of ML at various phases of the microbial NP drug discovery pipeline, discussing concrete examples throughout each major phase: genome mining, dereplication, and biological target prediction. Moreover, the authors discuss how ML approaches can be applied to semi-synthetic approaches to drug discovery. EXPERT OPINION Despite the important role that microbial NPs play in the development of novel drugs, their discovery has declined due to challenges associated with the conventional discovery process. ML is positioned to overcome these limitations given its ability to model complex datasets and generalize to novel chemical and sequence space. Unsurprisingly, ML comes with its own limitations that must be considered for its successful implementation. The authors stress the importance of continuing to build high quality and open access NP datasets to further increase the utility of ML in NP discovery.
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Affiliation(s)
- Autumn Arnold
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, OntarioCanada
- Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton,Ontario, Canada
- David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario Canada
| | - Jeremie Alexander
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, OntarioCanada
- Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton,Ontario, Canada
- David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario Canada
| | - Gary Liu
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, OntarioCanada
- Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton,Ontario, Canada
- David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario Canada
| | - Jonathan M Stokes
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, OntarioCanada
- Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton,Ontario, Canada
- David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario Canada
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