1
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Shi W, Yang H, Xie L, Yin XX, Zhang Y. A review of machine learning-based methods for predicting drug-target interactions. Health Inf Sci Syst 2024; 12:30. [PMID: 38617016 PMCID: PMC11014838 DOI: 10.1007/s13755-024-00287-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 03/04/2024] [Indexed: 04/16/2024] Open
Abstract
The prediction of drug-target interactions (DTI) is a crucial preliminary stage in drug discovery and development, given the substantial risk of failure and the prolonged validation period associated with in vitro and in vivo experiments. In the contemporary landscape, various machine learning-based methods have emerged as indispensable tools for DTI prediction. This paper begins by placing emphasis on the data representation employed by these methods, delineating five representations for drugs and four for proteins. The methods are then categorized into traditional machine learning-based approaches and deep learning-based ones, with a discussion of representative approaches in each category and the introduction of a novel taxonomy for deep neural network models in DTI prediction. Additionally, we present a synthesis of commonly used datasets and evaluation metrics to facilitate practical implementation. In conclusion, we address current challenges and outline potential future directions in this research field.
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Affiliation(s)
- Wen Shi
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006 China
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004 China
| | - Hong Yang
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006 China
| | - Linhai Xie
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Beijing, 102206 China
| | - Xiao-Xia Yin
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006 China
| | - Yanchun Zhang
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004 China
- Department of New Networks, Peng Cheng Laboratory, Shenzhen, 518000 China
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2
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Yang X, Yang G, Chu J. GraphCL-DTA: A Graph Contrastive Learning With Molecular Semantics for Drug-Target Binding Affinity Prediction. IEEE J Biomed Health Inform 2024; 28:4544-4552. [PMID: 38190664 DOI: 10.1109/jbhi.2024.3350666] [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: 01/10/2024]
Abstract
Drug-target binding affinity prediction plays an important role in the early stages of drug discovery, which can infer the strength of interactions between new drugs and new targets. However, the performance of previous computational models is limited by the following drawbacks. The learning of drug representation relies only on supervised data without considering the information in the molecular graph itself. Moreover, most previous studies tended to design complicated representation learning modules, while uniformity used to measure representation quality is ignored. In this study, we propose GraphCL-DTA, a graph contrastive learning with molecular semantics for drug-target binding affinity prediction. This graph contrastive learning framework replaces the dropout-based data augmentation strategy by performing data augmentation in the embedding space, thereby better preserving the semantic information of the molecular graph. A more essential and effective drug representation can be learned through this graph contrastive framework without additional supervised data. Next, we design a new loss function that can be directly used to adjust the uniformity of drug and target representations. By directly optimizing the uniformity of representations, the representation quality of drugs and targets can be improved. The effectiveness of the above innovative elements is verified on two real datasets, KIBA and Davis. Compared with the GraphDTA model, the relative improvement of the GraphCL-DTA model on the two datasets is 2.7% and 4.5%. The graph contrastive learning framework and uniformity function in the GraphCL-DTA model can be embedded into other computational models as independent modules to improve their generalization capability.
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3
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Chen Y, Liang X, Du W, Liang Y, Wong G, Chen L. Drug-Target Interaction Prediction Based on an Interactive Inference Network. Int J Mol Sci 2024; 25:7753. [PMID: 39062996 PMCID: PMC11277210 DOI: 10.3390/ijms25147753] [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: 05/16/2024] [Revised: 06/25/2024] [Accepted: 06/27/2024] [Indexed: 07/28/2024] Open
Abstract
Drug-target interactions underlie the actions of chemical substances in medicine. Moreover, drug repurposing can expand use profiles while reducing costs and development time by exploiting potential multi-functional pharmacological properties based upon additional target interactions. Nonetheless, drug repurposing relies on the accurate identification and validation of drug-target interactions (DTIs). In this study, a novel drug-target interaction prediction model was developed. The model, based on an interactive inference network, contains embedding, encoding, interaction, feature extraction, and output layers. In addition, this study used Morgan and PubChem molecular fingerprints as additional information for drug encoding. The interaction layer in our model simulates the drug-target interaction process, which assists in understanding the interaction by representing the interaction space. Our method achieves high levels of predictive performance, as well as interpretability of drug-target interactions. Additionally, we predicted and validated 22 Alzheimer's disease-related targets, suggesting our model is robust and effective and thus may be beneficial for drug repurposing.
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Affiliation(s)
- Yuqi Chen
- College of Mathematics and Computer, Shantou University, Shantou 515063, China; (Y.C.); (X.L.)
| | - Xiaomin Liang
- College of Mathematics and Computer, Shantou University, Shantou 515063, China; (Y.C.); (X.L.)
| | - Wei Du
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; (W.D.); (Y.L.)
| | - Yanchun Liang
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; (W.D.); (Y.L.)
| | - Garry Wong
- Faculty of Health Sciences, University of Macau, Taipa, Macau SAR 999078, China;
| | - Liang Chen
- College of Mathematics and Computer, Shantou University, Shantou 515063, China; (Y.C.); (X.L.)
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4
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Ranjan A, Bess A, Alvin C, Mukhopadhyay S. MDF-DTA: A Multi-Dimensional Fusion Approach for Drug-Target Binding Affinity Prediction. J Chem Inf Model 2024; 64:4980-4990. [PMID: 38888163 PMCID: PMC11234358 DOI: 10.1021/acs.jcim.4c00310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 05/15/2024] [Accepted: 05/29/2024] [Indexed: 06/20/2024]
Abstract
Drug-target affinity (DTA) prediction is an important task in the early stages of drug discovery. Traditional biological approaches are time-consuming, effort-consuming, and resource-consuming due to the large size of genomic and chemical spaces. Computational approaches using machine learning have emerged to narrow down the drug candidate search space. However, most of these prediction models focus on single feature encoding of drugs and targets, ignoring the importance of integrating different dimensions of these features. We propose a deep learning-based approach called Multi-Dimensional Fusion for Drug Target Affinity Prediction (MDF-DTA) incorporating different dimensional features. Our model fuses 1D, 2D, and 3D representations obtained from different pretrained models for both drugs and targets. We evaluated MDF-DTA on two standard benchmark data sets: DAVIS and KIBA. Experimental results show that MDF-DTA outperforms many state-of-the-art techniques in the DTA task across both data sets. Through ablation studies and performance evaluation metrics, we evaluate the importance of individual representations and the impact of each representation on MDF-DTA.
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Affiliation(s)
- Amit Ranjan
- Department
of Environmental Sciences, Louisiana State
University, Baton
Rouge, Louisiana 70803, United States
| | - Adam Bess
- Department
of Environmental Sciences, Louisiana State
University, Baton
Rouge, Louisiana 70803, United States
| | - Chris Alvin
- Department
of Computer Science, Furman University, Greenville, South Carolina 29613, United States
| | - Supratik Mukhopadhyay
- Department
of Environmental Sciences, Louisiana State
University, Baton
Rouge, Louisiana 70803, United States
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5
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Wei Z, Zhao C, Zhang M, Xu J, Xu N, Wu S, Xin X, Yu L, Feng W. PrCRS: a prediction model of severe CRS in CAR-T therapy based on transfer learning. BMC Bioinformatics 2024; 25:197. [PMID: 38769505 PMCID: PMC11106915 DOI: 10.1186/s12859-024-05804-8] [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: 02/06/2024] [Accepted: 05/08/2024] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND CAR-T cell therapy represents a novel approach for the treatment of hematologic malignancies and solid tumors. However, its implementation is accompanied by the emergence of potentially life-threatening adverse events known as cytokine release syndrome (CRS). Given the escalating number of patients undergoing CAR-T therapy, there is an urgent need to develop predictive models for severe CRS occurrence to prevent it in advance. Currently, all existing models are based on decision trees whose accuracy is far from meeting our expectations, and there is a lack of deep learning models to predict the occurrence of severe CRS more accurately. RESULTS We propose PrCRS, a deep learning prediction model based on U-net and Transformer. Given the limited data available for CAR-T patients, we employ transfer learning using data from COVID-19 patients. The comprehensive evaluation demonstrates the superiority of the PrCRS model over other state-of-the-art methods for predicting CRS occurrence. We propose six models to forecast the probability of severe CRS for patients with one, two, and three days in advance. Additionally, we present a strategy to convert the model's output into actual probabilities of severe CRS and provide corresponding predictions. CONCLUSIONS Based on our findings, PrCRS effectively predicts both the likelihood and timing of severe CRS in patients, thereby facilitating expedited and precise patient assessment, thus making a significant contribution to medical research. There is little research on applying deep learning algorithms to predict CRS, and our study fills this gap. This makes our research more novel and significant. Our code is publicly available at https://github.com/wzy38828201/PrCRS . The website of our prediction platform is: http://prediction.unicar-therapy.com/index-en.html .
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Affiliation(s)
- Zhenyu Wei
- Intelligent Systems Science and Engineering College, Harbin Engineering University, Liaoyuan Street, Harbin, 150006, Heilongjiang Province, People's Republic of China.
| | - Chengkui Zhao
- Intelligent Systems Science and Engineering College, Harbin Engineering University, Liaoyuan Street, Harbin, 150006, Heilongjiang Province, People's Republic of China
- Shanghai Unicar-Therapy BioMedicine Technology Co., Ltd, Shanghai, China
| | - Min Zhang
- Intelligent Systems Science and Engineering College, Harbin Engineering University, Liaoyuan Street, Harbin, 150006, Heilongjiang Province, People's Republic of China
| | - Jiayu Xu
- Intelligent Systems Science and Engineering College, Harbin Engineering University, Liaoyuan Street, Harbin, 150006, Heilongjiang Province, People's Republic of China
| | - Nan Xu
- School of Chemical and Molecular Engineering, East China Normal University, Zhongshan North Street, Shanghai, 200000, People's Republic of China
- Shanghai Unicar-Therapy BioMedicine Technology Co., Ltd, Shanghai, China
| | - Shiwei Wu
- Intelligent Systems Science and Engineering College, Harbin Engineering University, Liaoyuan Street, Harbin, 150006, Heilongjiang Province, People's Republic of China
| | - Xiaohui Xin
- Intelligent Systems Science and Engineering College, Harbin Engineering University, Liaoyuan Street, Harbin, 150006, Heilongjiang Province, People's Republic of China
| | - Lei Yu
- School of Chemical and Molecular Engineering, East China Normal University, Zhongshan North Street, Shanghai, 200000, People's Republic of China.
- Shanghai Unicar-Therapy BioMedicine Technology Co., Ltd, Shanghai, China.
| | - Weixing Feng
- Intelligent Systems Science and Engineering College, Harbin Engineering University, Liaoyuan Street, Harbin, 150006, Heilongjiang Province, People's Republic of China.
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6
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Zabihian A, Asghari J, Hooshmand M, Gharaghani S. A comparative analysis of computational drug repurposing approaches: proposing a novel tensor-matrix-tensor factorization method. Mol Divers 2024:10.1007/s11030-024-10851-7. [PMID: 38683487 DOI: 10.1007/s11030-024-10851-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 03/18/2024] [Indexed: 05/01/2024]
Abstract
Efficient drug discovery relies on drug repurposing, an important and open research field. This work presents a novel factorization method and a practical comparison of different approaches for drug repurposing. First, we propose a novel tensor-matrix-tensor (TMT) formulation as a new data array method with a gradient-based factorization procedure. Additionally, this paper examines and contrasts four computational drug repurposing approaches-factorization-based methods, machine learning methods, deep learning methods, and graph neural networks-to fulfill the second purpose. We test the strategies on two datasets and assess each approach's performance, drawbacks, problems, and benefits based on results. The results demonstrate that deep learning techniques work better than other strategies and that their results might be more reliable. Ultimately, graph neural methods need to be in an inductive manner to have a reliable prediction.
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Affiliation(s)
- Arash Zabihian
- Department of Bioinformatics, Kish International Campus, University of Tehran, Kish, Iran
| | - Javad Asghari
- Department of Computer Science and Information Technology, Institute of Advanced Studies in Basic Sciences, Zanjan, Iran
| | - Mohsen Hooshmand
- Department of Computer Science and Information Technology, Institute of Advanced Studies in Basic Sciences, Zanjan, Iran.
| | - Sajjad Gharaghani
- Laboratory of Bioinformatics and Drug Design, University of Tehran, Tehran, Iran
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7
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Jin S, Zhang Y, Yu H, Lu M. SADR: Self-Supervised Graph Learning With Adaptive Denoising for Drug Repositioning. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:265-277. [PMID: 38190661 DOI: 10.1109/tcbb.2024.3351079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2024]
Abstract
Traditional drug development is often high-risk and time-consuming. A promising alternative is to reuse or relocate approved drugs. Recently, some methods based on graph representation learning have started to be used for drug repositioning. These models learn the low dimensional embeddings of drug and disease nodes from the drug-disease interaction network to predict the potential association between drugs and diseases. However, these methods have strict requirements for the dataset, and if the dataset is sparse, the performance of these methods will be severely affected. At the same time, these methods have poor robustness to noise in the dataset. In response to the above challenges, we propose a drug repositioning model based on self-supervised graph learning with adptive denoising, called SADR. SADR uses data augmentation and contrastive learning strategies to learn feature representations of nodes, which can effectively solve the problems caused by sparse datasets. SADR includes an adaptive denoising training (ADT) component that can effectively identify noisy data during the training process and remove the impact of noise on the model. We have conducted comprehensive experiments on three datasets and have achieved better prediction accuracy compared to multiple baseline models. At the same time, we propose the top 10 new predictive approved drugs for treating two diseases. This demonstrates the ability of our model to identify potential drug candidates for disease indications.
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8
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Dehghan A, Abbasi K, Razzaghi P, Banadkuki H, Gharaghani S. CCL-DTI: contributing the contrastive loss in drug-target interaction prediction. BMC Bioinformatics 2024; 25:48. [PMID: 38291364 PMCID: PMC11264960 DOI: 10.1186/s12859-024-05671-3] [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: 09/13/2023] [Accepted: 01/22/2024] [Indexed: 02/01/2024] Open
Abstract
BACKGROUND The Drug-Target Interaction (DTI) prediction uses a drug molecule and a protein sequence as inputs to predict the binding affinity value. In recent years, deep learning-based models have gotten more attention. These methods have two modules: the feature extraction module and the task prediction module. In most deep learning-based approaches, a simple task prediction loss (i.e., categorical cross entropy for the classification task and mean squared error for the regression task) is used to learn the model. In machine learning, contrastive-based loss functions are developed to learn more discriminative feature space. In a deep learning-based model, extracting more discriminative feature space leads to performance improvement for the task prediction module. RESULTS In this paper, we have used multimodal knowledge as input and proposed an attention-based fusion technique to combine this knowledge. Also, we investigate how utilizing contrastive loss function along the task prediction loss could help the approach to learn a more powerful model. Four contrastive loss functions are considered: (1) max-margin contrastive loss function, (2) triplet loss function, (3) Multi-class N-pair Loss Objective, and (4) NT-Xent loss function. The proposed model is evaluated using four well-known datasets: Wang et al. dataset, Luo's dataset, Davis, and KIBA datasets. CONCLUSIONS Accordingly, after reviewing the state-of-the-art methods, we developed a multimodal feature extraction network by combining protein sequences and drug molecules, along with protein-protein interaction networks and drug-drug interaction networks. The results show it performs significantly better than the comparable state-of-the-art approaches.
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Affiliation(s)
- Alireza Dehghan
- Department of Bioinformatics, Kish International Campus, University of Tehran, Kish, 1417614411, Iran
| | - Karim Abbasi
- Laboratory of System Biology, Bioinformatics and Artificial Intelligence in Medicine (LBB&AI), Faculty of Mathematics and Computer Science, Kharazmi University, Tehran, 1417614411, Iran
| | - Parvin Razzaghi
- Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, 4513766731, Iran.
| | - Hossein Banadkuki
- Laboratory of Bioinformatics and Drug Design (LBD), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, 1417614411, Iran
| | - Sajjad Gharaghani
- Laboratory of Bioinformatics and Drug Design (LBD), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, 1417614411, Iran.
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9
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Wu H, Liu J, Jiang T, Zou Q, Qi S, Cui Z, Tiwari P, Ding Y. AttentionMGT-DTA: A multi-modal drug-target affinity prediction using graph transformer and attention mechanism. Neural Netw 2024; 169:623-636. [PMID: 37976593 DOI: 10.1016/j.neunet.2023.11.018] [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: 06/26/2023] [Revised: 09/29/2023] [Accepted: 11/07/2023] [Indexed: 11/19/2023]
Abstract
The accurate prediction of drug-target affinity (DTA) is a crucial step in drug discovery and design. Traditional experiments are very expensive and time-consuming. Recently, deep learning methods have achieved notable performance improvements in DTA prediction. However, one challenge for deep learning-based models is appropriate and accurate representations of drugs and targets, especially the lack of effective exploration of target representations. Another challenge is how to comprehensively capture the interaction information between different instances, which is also important for predicting DTA. In this study, we propose AttentionMGT-DTA, a multi-modal attention-based model for DTA prediction. AttentionMGT-DTA represents drugs and targets by a molecular graph and binding pocket graph, respectively. Two attention mechanisms are adopted to integrate and interact information between different protein modalities and drug-target pairs. The experimental results showed that our proposed model outperformed state-of-the-art baselines on two benchmark datasets. In addition, AttentionMGT-DTA also had high interpretability by modeling the interaction strength between drug atoms and protein residues. Our code is available at https://github.com/JK-Liu7/AttentionMGT-DTA.
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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; Yangtze Delta Region Institute(Quzhou), University of Electronic Science and Technology of China, Quzhou, 324003, China.
| | - Tengsheng Jiang
- Gusu School, Nanjing Medical University, Suzhou, 215009, China.
| | - Quan Zou
- Yangtze Delta Region Institute(Quzhou), University of Electronic Science and Technology of China, Quzhou, 324003, China.
| | - Shujie Qi
- 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.
| | - Prayag Tiwari
- School of Information Technology, Halmstad University, Sweden.
| | - Yijie Ding
- Yangtze Delta Region Institute(Quzhou), University of Electronic Science and Technology of China, Quzhou, 324003, China.
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10
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Abdul Raheem AK, Dhannoon BN. Comprehensive Review on Drug-target Interaction Prediction - Latest Developments and Overview. Curr Drug Discov Technol 2024; 21:e010923220652. [PMID: 37680152 DOI: 10.2174/1570163820666230901160043] [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: 01/23/2023] [Revised: 05/29/2023] [Accepted: 07/18/2023] [Indexed: 09/09/2023]
Abstract
Drug-target interactions (DTIs) are an important part of the drug development process. When the drug (a chemical molecule) binds to a target (proteins or nucleic acids), it modulates the biological behavior/function of the target, returning it to its normal state. Predicting DTIs plays a vital role in the drug discovery (DD) process as it has the potential to enhance efficiency and reduce costs. However, DTI prediction poses significant challenges and expenses due to the time-consuming and costly nature of experimental assays. As a result, researchers have increased their efforts to identify the association between medications and targets in the hopes of speeding up drug development and shortening the time to market. This paper provides a detailed discussion of the initial stage in drug discovery, namely drug-target interactions. It focuses on exploring the application of machine learning methods within this step. Additionally, we aim to conduct a comprehensive review of relevant papers and databases utilized in this field. Drug target interaction prediction covers a wide range of applications: drug discovery, prediction of adverse effects and drug repositioning. The prediction of drugtarget interactions can be categorized into three main computational methods: docking simulation approaches, ligand-based methods, and machine-learning techniques.
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Affiliation(s)
- Ali K Abdul Raheem
- Software Department, College of Information Technology, University of Babylon, Hillah, Babil, Iraq
- University of Warith Al-Anbiyaa, Kerbala, Iraq
| | - Ban N Dhannoon
- Department of Computer Science, College of Science, Al-Nahrain University, Baghdad, Iraq
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11
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Zhao BW, Su XR, Yang Y, Li DX, Li GD, Hu PW, Zhao YG, Hu L. Drug-disease association prediction using semantic graph and function similarity representation learning over heterogeneous information networks. Methods 2023; 220:106-114. [PMID: 37972913 DOI: 10.1016/j.ymeth.2023.10.014] [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: 06/30/2023] [Revised: 10/13/2023] [Accepted: 10/28/2023] [Indexed: 11/19/2023] Open
Abstract
Discovering new indications for existing drugs is a promising development strategy at various stages of drug research and development. However, most of them complete their tasks by constructing a variety of heterogeneous networks without considering available higher-order connectivity patterns in heterogeneous biological information networks, which are believed to be useful for improving the accuracy of new drug discovering. To this end, we propose a computational-based model, called SFRLDDA, for drug-disease association prediction by using semantic graph and function similarity representation learning. Specifically, SFRLDDA first integrates a heterogeneous information network (HIN) by drug-disease, drug-protein, protein-disease associations, and their biological knowledge. Second, different representation learning strategies are applied to obtain the feature representations of drugs and diseases from different perspectives over semantic graph and function similarity graphs constructed, respectively. At last, a Random Forest classifier is incorporated by SFRLDDA to discover potential drug-disease associations (DDAs). Experimental results demonstrate that SFRLDDA yields a best performance when compared with other state-of-the-art models on three benchmark datasets. Moreover, case studies also indicate that the simultaneous consideration of semantic graph and function similarity of drugs and diseases in the HIN allows SFRLDDA to precisely predict DDAs in a more comprehensive manner.
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Affiliation(s)
- Bo-Wei Zhao
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
| | - Xiao-Rui Su
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
| | - Yue Yang
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
| | - Dong-Xu Li
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
| | - Guo-Dong Li
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
| | - Peng-Wei Hu
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
| | - Yong-Gang Zhao
- Department of Orthopaedic Surgery (hand and foot trauma), People's Hospital of Dongxihu, Wuhan 420100, China.
| | - Lun Hu
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
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12
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Zhu Z, Yao Z, Zheng X, Qi G, Li Y, Mazur N, Gao X, Gong Y, Cong B. Drug-target affinity prediction method based on multi-scale information interaction and graph optimization. Comput Biol Med 2023; 167:107621. [PMID: 37907030 DOI: 10.1016/j.compbiomed.2023.107621] [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: 07/18/2023] [Revised: 10/16/2023] [Accepted: 10/23/2023] [Indexed: 11/02/2023]
Abstract
Drug-target affinity (DTA) prediction as an emerging and effective method is widely applied to explore the strength of drug-target interactions in drug development research. By predicting these interactions, researchers can assess the potential efficacy and safety of candidate drugs at an early stage, narrowing down the search space for therapeutic targets and accelerating the discovery and development of new drugs. However, existing DTA prediction models mainly use graphical representations of drug molecules, which lack information on interactions between individual substructures, thus affecting prediction accuracy and model interpretability. Therefore, transformer and diffusion on drug graphs in DTA prediction (TDGraphDTA) are introduced to predict drug-target interactions using multi-scale information interaction and graph optimization. An interactive module is integrated into feature extraction of drug and target features at different granularity levels. A diffusion model-based graph optimization module is proposed to improve the representation of molecular graph structures and enhance the interpretability of graph representations while obtaining optimal feature representations. In addition, TDGraphDTA improves the accuracy and reliability of predictions by capturing relationships and contextual information between molecular substructures. The performance of the proposed TDGraphDTA in DTA prediction was verified on three publicly available benchmark datasets (Davis, Metz, and KIBA). Compared with state-of-the-art baseline models, it achieved better results in terms of consistency index, R-squared, etc. Furthermore, compared with some existing methods, the proposed TDGraphDTA is demonstrated to have better structure capturing capabilities by visualizing the feature capturing capabilities of the model using Grad-AAM toxicity labels in the ToxCast dataset. The corresponding source codes are available at https://github.com/Lamouryz/TDGraph.
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Affiliation(s)
- Zhiqin Zhu
- College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
| | - Zheng Yao
- College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
| | - Xin Zheng
- College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
| | - Guanqiu Qi
- Computer Information Systems Department, State University of New York at Buffalo State, Buffalo, NY 14222, USA.
| | - Yuanyuan Li
- College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
| | - Neal Mazur
- Computer Information Systems Department, State University of New York at Buffalo State, Buffalo, NY 14222, USA.
| | - Xinbo Gao
- College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
| | - Yifei Gong
- Faculty of applied science & engineering, the Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto at Toronto, ON M5S, Canada.
| | - Baisen Cong
- Diagnostics Digital, DH(Shanghai) Diagnostics Co, Ltd, a Danaher company, Shanghai, 200335, China.
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13
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Meng Y, Wang Y, Xu J, Lu C, Tang X, Peng T, Zhang B, Tian G, Yang J. Drug repositioning based on weighted local information augmented graph neural network. Brief Bioinform 2023; 25:bbad431. [PMID: 38019732 PMCID: PMC10686358 DOI: 10.1093/bib/bbad431] [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: 08/26/2023] [Revised: 10/13/2023] [Accepted: 11/05/2023] [Indexed: 12/01/2023] Open
Abstract
Drug repositioning, the strategy of redirecting existing drugs to new therapeutic purposes, is pivotal in accelerating drug discovery. While many studies have engaged in modeling complex drug-disease associations, they often overlook the relevance between different node embeddings. Consequently, we propose a novel weighted local information augmented graph neural network model, termed DRAGNN, for drug repositioning. Specifically, DRAGNN firstly incorporates a graph attention mechanism to dynamically allocate attention coefficients to drug and disease heterogeneous nodes, enhancing the effectiveness of target node information collection. To prevent excessive embedding of information in a limited vector space, we omit self-node information aggregation, thereby emphasizing valuable heterogeneous and homogeneous information. Additionally, average pooling in neighbor information aggregation is introduced to enhance local information while maintaining simplicity. A multi-layer perceptron is then employed to generate the final association predictions. The model's effectiveness for drug repositioning is supported by a 10-times 10-fold cross-validation on three benchmark datasets. Further validation is provided through analysis of the predicted associations using multiple authoritative data sources, molecular docking experiments and drug-disease network analysis, laying a solid foundation for future drug discovery.
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Affiliation(s)
- Yajie Meng
- Center of Applied Mathematics & Interdisciplinary Science, School of Mathematical & Physical Sciences, Wuhan Textile University, No. 1, Yangguang Avenue, Jiangxia District, Wuhan City, Hubei Province 430200, China
| | - Yi Wang
- Center of Applied Mathematics & Interdisciplinary Science, School of Mathematical & Physical Sciences, Wuhan Textile University, No. 1, Yangguang Avenue, Jiangxia District, Wuhan City, Hubei Province 430200, China
| | - Junlin Xu
- College of Computer Science and Electronic Engineering, Hunan University, Lushan Road (S), Yuelu District, Changsha, Hunan Province 410082, China
| | - Changcheng Lu
- College of Computer Science and Electronic Engineering, Hunan University, Lushan Road (S), Yuelu District, Changsha, Hunan Province 410082, China
| | - Xianfang Tang
- Center of Applied Mathematics & Interdisciplinary Science, School of Mathematical & Physical Sciences, Wuhan Textile University, No. 1, Yangguang Avenue, Jiangxia District, Wuhan City, Hubei Province 430200, China
| | - Tao Peng
- Center of Applied Mathematics & Interdisciplinary Science, School of Mathematical & Physical Sciences, Wuhan Textile University, No. 1, Yangguang Avenue, Jiangxia District, Wuhan City, Hubei Province 430200, China
| | - Bengong Zhang
- Center of Applied Mathematics & Interdisciplinary Science, School of Mathematical & Physical Sciences, Wuhan Textile University, No. 1, Yangguang Avenue, Jiangxia District, Wuhan City, Hubei Province 430200, China
| | - Geng Tian
- Geneis Beijing Co., Ltd, No. 31, New North Road, Laiguanying, Chaoyang District, Beijing 100102, China
| | - Jialiang Yang
- Geneis Beijing Co., Ltd, No. 31, New North Road, Laiguanying, Chaoyang District, Beijing 100102, China
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14
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Liu H, Yu S, Li X, Wang X, Qi D, Pan F, Chai X, Wang Q, Pan Y, Zhang L, Liu Y. Integration of Deep Learning and Sequential Metabolism to Rapidly Screen Dipeptidyl Peptidase (DPP)-IV Inhibitors from Gardenia jasminoides Ellis. Molecules 2023; 28:7381. [PMID: 37959800 PMCID: PMC10649927 DOI: 10.3390/molecules28217381] [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: 10/01/2023] [Revised: 10/24/2023] [Accepted: 10/27/2023] [Indexed: 11/15/2023] Open
Abstract
Traditional Chinese medicine (TCM) possesses unique advantages in the management of blood glucose and lipids. However, there is still a significant gap in the exploration of its pharmacologically active components. Integrated strategies encompassing deep-learning prediction models and active validation based on absorbable ingredients can greatly improve the identification rate and screening efficiency in TCM. In this study, the affinity prediction of 11,549 compounds from the traditional Chinese medicine system's pharmacology database (TCMSP) with dipeptidyl peptidase-IV (DPP-IV) based on a deep-learning model was firstly conducted. With the results, Gardenia jasminoides Ellis (GJE), a food medicine with homologous properties, was selected as a model drug. The absorbed components of GJE were subsequently identified through in vivo intestinal perfusion and oral administration. As a result, a total of 38 prototypical absorbed components of GJE were identified. These components were analyzed to determine their absorption patterns after intestinal, hepatic, and systemic metabolism. Virtual docking and DPP-IV enzyme activity experiments were further conducted to validate the inhibitory effects and potential binding sites of the common constituents of deep learning and sequential metabolism. The results showed a significant DPP-IV inhibitory activity (IC50 53 ± 0.63 μg/mL) of the iridoid glycosides' potent fractions, which is a novel finding. Genipin 1-gentiobioside was screened as a promising new DPP-IV inhibitor in GJE. These findings highlight the potential of this innovative approach for the rapid screening of active ingredients in TCM and provide insights into the molecular mechanisms underlying the anti-diabetic activity of GJE.
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Affiliation(s)
- Huining Liu
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China; (H.L.); (S.Y.); (X.L.); (X.W.); (D.Q.); (F.P.); (X.C.); (Q.W.)
| | - Shuang Yu
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China; (H.L.); (S.Y.); (X.L.); (X.W.); (D.Q.); (F.P.); (X.C.); (Q.W.)
| | - Xueyan Li
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China; (H.L.); (S.Y.); (X.L.); (X.W.); (D.Q.); (F.P.); (X.C.); (Q.W.)
| | - Xinyu Wang
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China; (H.L.); (S.Y.); (X.L.); (X.W.); (D.Q.); (F.P.); (X.C.); (Q.W.)
| | - Dongying Qi
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China; (H.L.); (S.Y.); (X.L.); (X.W.); (D.Q.); (F.P.); (X.C.); (Q.W.)
| | - Fulu Pan
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China; (H.L.); (S.Y.); (X.L.); (X.W.); (D.Q.); (F.P.); (X.C.); (Q.W.)
| | - Xiaoyu Chai
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China; (H.L.); (S.Y.); (X.L.); (X.W.); (D.Q.); (F.P.); (X.C.); (Q.W.)
| | - Qianqian Wang
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China; (H.L.); (S.Y.); (X.L.); (X.W.); (D.Q.); (F.P.); (X.C.); (Q.W.)
| | - Yanli Pan
- Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Lei Zhang
- Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing 100191, China
| | - Yang Liu
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China; (H.L.); (S.Y.); (X.L.); (X.W.); (D.Q.); (F.P.); (X.C.); (Q.W.)
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15
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Zeng L, Gu R, Li W, Shao Y, Zhu Y, Xie Z, Liu H, Zhou Y. Ataluren prevented bone loss induced by ovariectomy and aging in mice through the BMP-SMAD signaling pathway. Biomed Pharmacother 2023; 166:115332. [PMID: 37597324 DOI: 10.1016/j.biopha.2023.115332] [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: 05/18/2023] [Revised: 08/02/2023] [Accepted: 08/13/2023] [Indexed: 08/21/2023] Open
Abstract
Both estrogen deficiency and aging may lead to osteoporosis. Developing novel drugs for treating osteoporosis is a popular research direction. We screened several potential therapeutic agents through a new deep learning-based efficacy prediction system (DLEPS) using transcriptional profiles for osteoporosis. DLEPS screening led to a potential novel drug examinee, ataluren, for treating osteoporosis. Ataluren significantly reversed bone loss in ovariectomized mice. Next, ataluren significantly increased human bone marrow-derived mesenchymal stem cell (hBMMSC) osteogenic differentiation without cytotoxicity, indicated by the high expression index of osteogenic differentiation genes (OCN , BGLAP, ALP, COL1A, BMP2, RUNX2). Mechanistically, ataluren exerted its function through the BMP-SMAD pathway. Furthermore, it activated SMAD phosphorylation but osteogenic differentiation was attenuated by BMP2-SMAD inhibitors or small interfering RNA of BMP2. Finally, ataluren significantly reversed bone loss in aged mice. In summary, our findings suggest that the DLEPS-screened ataluren may be a therapeutic agent against osteoporosis by aiding hBMMSC osteogenic differentiation.
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Affiliation(s)
- Lijun Zeng
- Department of Prosthodontics, Peking University School and Hospital of Stomatology, Beijing 100081, China; National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & National Health Commission Key Laboratory of Digital Technology of Stomatology, Beijing 100081, China
| | - Ranli Gu
- Department of Prosthodontics, Peking University School and Hospital of Stomatology, Beijing 100081, China; National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & National Health Commission Key Laboratory of Digital Technology of Stomatology, Beijing 100081, China
| | - Wei Li
- Department of Oral Pathology, Peking University School and Hospital of Stomatology, Beijing 100081, China; National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & National Health Commission Key Laboratory of Digital Technology of Stomatology, Beijing 100081, China
| | - Yuzi Shao
- Department of Prosthodontics, Peking University School and Hospital of Stomatology, Beijing 100081, China; National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & National Health Commission Key Laboratory of Digital Technology of Stomatology, Beijing 100081, China
| | - Yuan Zhu
- Department of Prosthodontics, Peking University School and Hospital of Stomatology, Beijing 100081, China; National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & National Health Commission Key Laboratory of Digital Technology of Stomatology, Beijing 100081, China
| | - Zhengwei Xie
- Peking University International Cancer Institute, Peking University Health Science Center, Peking University, 38 Xueyuan Lu, Haidian District, Beijing 100191, China.
| | - Hao Liu
- Central Laboratory, Peking University School and Hospital of Stomatology, Beijing 100081, China; National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & National Health Commission Key Laboratory of Digital Technology of Stomatology, Beijing 100081, China.
| | - Yongsheng Zhou
- Department of Prosthodontics, Peking University School and Hospital of Stomatology, Beijing 100081, China; National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & National Health Commission Key Laboratory of Digital Technology of Stomatology, Beijing 100081, China.
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16
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Wang L, Zhou Y, Chen Q. AMMVF-DTI: A Novel Model Predicting Drug-Target Interactions Based on Attention Mechanism and Multi-View Fusion. Int J Mol Sci 2023; 24:14142. [PMID: 37762445 PMCID: PMC10531525 DOI: 10.3390/ijms241814142] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 09/09/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
Accurate identification of potential drug-target interactions (DTIs) is a crucial task in drug development and repositioning. Despite the remarkable progress achieved in recent years, improving the performance of DTI prediction still presents significant challenges. In this study, we propose a novel end-to-end deep learning model called AMMVF-DTI (attention mechanism and multi-view fusion), which leverages a multi-head self-attention mechanism to explore varying degrees of interaction between drugs and target proteins. More importantly, AMMVF-DTI extracts interactive features between drugs and proteins from both node-level and graph-level embeddings, enabling a more effective modeling of DTIs. This advantage is generally lacking in existing DTI prediction models. Consequently, when compared to many of the start-of-the-art methods, AMMVF-DTI demonstrated excellent performance on the human, C. elegans, and DrugBank baseline datasets, which can be attributed to its ability to incorporate interactive information and mine features from both local and global structures. The results from additional ablation experiments also confirmed the importance of each module in our AMMVF-DTI model. Finally, a case study is presented utilizing our model for COVID-19-related DTI prediction. We believe the AMMVF-DTI model can not only achieve reasonable accuracy in DTI prediction, but also provide insights into the understanding of potential interactions between drugs and targets.
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17
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Huang Y, Huang HY, Chen Y, Lin YCD, Yao L, Lin T, Leng J, Chang Y, Zhang Y, Zhu Z, Ma K, Cheng YN, Lee TY, Huang HD. A Robust Drug-Target Interaction Prediction Framework with Capsule Network and Transfer Learning. Int J Mol Sci 2023; 24:14061. [PMID: 37762364 PMCID: PMC10531393 DOI: 10.3390/ijms241814061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 08/27/2023] [Accepted: 08/28/2023] [Indexed: 09/29/2023] Open
Abstract
Drug-target interactions (DTIs) are considered a crucial component of drug design and drug discovery. To date, many computational methods were developed for drug-target interactions, but they are insufficiently informative for accurately predicting DTIs due to the lack of experimentally verified negative datasets, inaccurate molecular feature representation, and ineffective DTI classifiers. Therefore, we address the limitations of randomly selecting negative DTI data from unknown drug-target pairs by establishing two experimentally validated datasets and propose a capsule network-based framework called CapBM-DTI to capture hierarchical relationships of drugs and targets, which adopts pre-trained bidirectional encoder representations from transformers (BERT) for contextual sequence feature extraction from target proteins through transfer learning and the message-passing neural network (MPNN) for the 2-D graph feature extraction of compounds to accurately and robustly identify drug-target interactions. We compared the performance of CapBM-DTI with state-of-the-art methods using four experimentally validated DTI datasets of different sizes, including human (Homo sapiens) and worm (Caenorhabditis elegans) species datasets, as well as three subsets (new compounds, new proteins, and new pairs). Our results demonstrate that the proposed model achieved robust performance and powerful generalization ability in all experiments. The case study on treating COVID-19 demonstrates the applicability of the model in virtual screening.
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Affiliation(s)
- Yixian Huang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.H.); (Y.C.); (J.L.)
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (L.Y.); (Y.C.)
| | - Hsi-Yuan Huang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.H.); (Y.C.); (J.L.)
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (L.Y.); (Y.C.)
| | - Yigang Chen
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.H.); (Y.C.); (J.L.)
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (L.Y.); (Y.C.)
| | - Yang-Chi-Dung Lin
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.H.); (Y.C.); (J.L.)
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (L.Y.); (Y.C.)
| | - Lantian Yao
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (L.Y.); (Y.C.)
| | - Tianxiu Lin
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.H.); (Y.C.); (J.L.)
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (L.Y.); (Y.C.)
| | - Junlin Leng
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.H.); (Y.C.); (J.L.)
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (L.Y.); (Y.C.)
| | - Yuan Chang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (L.Y.); (Y.C.)
| | - Yuntian Zhang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (L.Y.); (Y.C.)
| | - Zihao Zhu
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.H.); (Y.C.); (J.L.)
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (L.Y.); (Y.C.)
| | - Kun Ma
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.H.); (Y.C.); (J.L.)
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (L.Y.); (Y.C.)
| | - Yeong-Nan Cheng
- Institute of Bioinformatics and Systems Biology, Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan; (Y.-N.C.)
| | - Tzong-Yi Lee
- Institute of Bioinformatics and Systems Biology, Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan; (Y.-N.C.)
| | - Hsien-Da Huang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.H.); (Y.C.); (J.L.)
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (L.Y.); (Y.C.)
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18
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Qian Y, Li X, Wu J, Zhang Q. MCL-DTI: using drug multimodal information and bi-directional cross-attention learning method for predicting drug-target interaction. BMC Bioinformatics 2023; 24:323. [PMID: 37633938 PMCID: PMC10463755 DOI: 10.1186/s12859-023-05447-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 08/15/2023] [Indexed: 08/28/2023] Open
Abstract
BACKGROUND Prediction of drug-target interaction (DTI) is an essential step for drug discovery and drug reposition. Traditional methods are mostly time-consuming and labor-intensive, and deep learning-based methods address these limitations and are applied to engineering. Most of the current deep learning methods employ representation learning of unimodal information such as SMILES sequences, molecular graphs, or molecular images of drugs. In addition, most methods focus on feature extraction from drug and target alone without fusion learning from drug-target interacting parties, which may lead to insufficient feature representation. MOTIVATION In order to capture more comprehensive drug features, we utilize both molecular image and chemical features of drugs. The image of the drug mainly has the structural information and spatial features of the drug, while the chemical information includes its functions and properties, which can complement each other, making drug representation more effective and complete. Meanwhile, to enhance the interactive feature learning of drug and target, we introduce a bidirectional multi-head attention mechanism to improve the performance of DTI. RESULTS To enhance feature learning between drugs and targets, we propose a novel model based on deep learning for DTI task called MCL-DTI which uses multimodal information of drug and learn the representation of drug-target interaction for drug-target prediction. In order to further explore a more comprehensive representation of drug features, this paper first exploits two multimodal information of drugs, molecular image and chemical text, to represent the drug. We also introduce to use bi-rectional multi-head corss attention (MCA) method to learn the interrelationships between drugs and targets. Thus, we build two decoders, which include an multi-head self attention (MSA) block and an MCA block, for cross-information learning. We use a decoder for the drug and target separately to obtain the interaction feature maps. Finally, we feed these feature maps generated by decoders into a fusion block for feature extraction and output the prediction results. CONCLUSIONS MCL-DTI achieves the best results in all the three datasets: Human, C. elegans and Davis, including the balanced datasets and an unbalanced dataset. The results on the drug-drug interaction (DDI) task show that MCL-DTI has a strong generalization capability and can be easily applied to other tasks.
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Affiliation(s)
- Ying Qian
- Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Computer Science and Technology, East China Normal University, North Zhongshan Road, Shanghai, 200062 China
| | - Xinyi Li
- Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Computer Science and Technology, East China Normal University, North Zhongshan Road, Shanghai, 200062 China
| | - Jian Wu
- Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Computer Science and Technology, East China Normal University, North Zhongshan Road, Shanghai, 200062 China
| | - Qian Zhang
- Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Computer Science and Technology, East China Normal University, North Zhongshan Road, Shanghai, 200062 China
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19
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Binatlı OC, Gönen M. MOKPE: drug-target interaction prediction via manifold optimization based kernel preserving embedding. BMC Bioinformatics 2023; 24:276. [PMID: 37407927 DOI: 10.1186/s12859-023-05401-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 06/25/2023] [Indexed: 07/07/2023] Open
Abstract
BACKGROUND In many applications of bioinformatics, data stem from distinct heterogeneous sources. One of the well-known examples is the identification of drug-target interactions (DTIs), which is of significant importance in drug discovery. In this paper, we propose a novel framework, manifold optimization based kernel preserving embedding (MOKPE), to efficiently solve the problem of modeling heterogeneous data. Our model projects heterogeneous drug and target data into a unified embedding space by preserving drug-target interactions and drug-drug, target-target similarities simultaneously. RESULTS We performed ten replications of ten-fold cross validation on four different drug-target interaction network data sets for predicting DTIs for previously unseen drugs. The classification evaluation metrics showed better or comparable performance compared to previous similarity-based state-of-the-art methods. We also evaluated MOKPE on predicting unknown DTIs of a given network. Our implementation of the proposed algorithm in R together with the scripts that replicate the reported experiments is publicly available at https://github.com/ocbinatli/mokpe .
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Affiliation(s)
- Oğuz C Binatlı
- Graduate School of Sciences and Engineering, Koç University, 34450, Istanbul, Turkey
| | - Mehmet Gönen
- Department of Industrial Engineering, College of Engineering, Koç University, 34450, Istanbul, Turkey.
- School of Medicine, Koç University, 34450, Istanbul, Turkey.
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20
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Dalkıran A, Atakan A, Rifaioğlu AS, Martin MJ, Atalay RÇ, Acar AC, Doğan T, Atalay V. Transfer learning for drug-target interaction prediction. Bioinformatics 2023; 39:i103-i110. [PMID: 37387156 DOI: 10.1093/bioinformatics/btad234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/19/2023] [Indexed: 07/01/2023] Open
Abstract
MOTIVATION Utilizing AI-driven approaches for drug-target interaction (DTI) prediction require large volumes of training data which are not available for the majority of target proteins. In this study, we investigate the use of deep transfer learning for the prediction of interactions between drug candidate compounds and understudied target proteins with scarce training data. The idea here is to first train a deep neural network classifier with a generalized source training dataset of large size and then to reuse this pre-trained neural network as an initial configuration for re-training/fine-tuning purposes with a small-sized specialized target training dataset. To explore this idea, we selected six protein families that have critical importance in biomedicine: kinases, G-protein-coupled receptors (GPCRs), ion channels, nuclear receptors, proteases, and transporters. In two independent experiments, the protein families of transporters and nuclear receptors were individually set as the target datasets, while the remaining five families were used as the source datasets. Several size-based target family training datasets were formed in a controlled manner to assess the benefit provided by the transfer learning approach. RESULTS Here, we present a systematic evaluation of our approach by pre-training a feed-forward neural network with source training datasets and applying different modes of transfer learning from the pre-trained source network to a target dataset. The performance of deep transfer learning is evaluated and compared with that of training the same deep neural network from scratch. We found that when the training dataset contains fewer than 100 compounds, transfer learning outperforms the conventional strategy of training the system from scratch, suggesting that transfer learning is advantageous for predicting binders to under-studied targets. AVAILABILITY AND IMPLEMENTATION The source code and datasets are available at https://github.com/cansyl/TransferLearning4DTI. Our web-based service containing the ready-to-use pre-trained models is accessible at https://tl4dti.kansil.org.
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Affiliation(s)
- Alperen Dalkıran
- Department of Computer Engineering, Middle East Technical University, Ankara 06800, Turkey
- Department of Computer Engineering, Adana Alparslan Türkeş Science and Technology University, Adana 01250, Turkey
| | - Ahmet Atakan
- Department of Computer Engineering, Middle East Technical University, Ankara 06800, Turkey
- Department of Computer Engineering, Erzincan Binali Yıldırım University, Erzincan 24002, Turkey
| | - Ahmet S Rifaioğlu
- Department of Computer Engineering, Iskenderun Technical University, Hatay 31200, Turkey
- Faculty of Medicine, Institute for Computational Biomedicine, Heidelberg University and Heidelberg University Hospital, Heidelberg 69120, Germany
| | - Maria J Martin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, Hinxton CB10 1SD, United Kingdom
| | - Rengül Çetin Atalay
- Faculty of Pulmonary and Critical Care Medicine, the University of Chicago, Chicago, IL, 60637, United States
| | - Aybar C Acar
- Cancer Systems Biology Laboratory (Kansil), Middle East Technical University, Ankara 06800, Turkey
| | - Tunca Doğan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, Hinxton CB10 1SD, United Kingdom
- Department of Computer Engineering, Hacettepe University, Ankara 06800, Turkey
| | - Volkan Atalay
- Department of Computer Engineering, Middle East Technical University, Ankara 06800, Turkey
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Zabihian A, Sayyad FZ, Hashemi SM, Shami Tanha R, Hooshmand M, Gharaghani S. DEDTI versus IEDTI: efficient and predictive models of drug-target interactions. Sci Rep 2023; 13:9238. [PMID: 37286613 DOI: 10.1038/s41598-023-36438-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 06/03/2023] [Indexed: 06/09/2023] Open
Abstract
Drug repurposing is an active area of research that aims to decrease the cost and time of drug development. Most of those efforts are primarily concerned with the prediction of drug-target interactions. Many evaluation models, from matrix factorization to more cutting-edge deep neural networks, have come to the scene to identify such relations. Some predictive models are devoted to the prediction's quality, and others are devoted to the efficiency of the predictive models, e.g., embedding generation. In this work, we propose new representations of drugs and targets useful for more prediction and analysis. Using these representations, we propose two inductive, deep network models of IEDTI and DEDTI for drug-target interaction prediction. Both of them use the accumulation of new representations. The IEDTI takes advantage of triplet and maps the input accumulated similarity features into meaningful embedding corresponding vectors. Then, it applies a deep predictive model to each drug-target pair to evaluate their interaction. The DEDTI directly uses the accumulated similarity feature vectors of drugs and targets and applies a predictive model on each pair to identify their interactions. We have done a comprehensive simulation on the DTINet dataset as well as gold standard datasets, and the results show that DEDTI outperforms IEDTI and the state-of-the-art models. In addition, we conduct a docking study on new predicted interactions between two drug-target pairs, and the results confirm acceptable drug-target binding affinity between both predicted pairs.
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Affiliation(s)
- Arash Zabihian
- Laboratory of Bioinformatics and Drug Design (LBD), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
- Department of Bioinformatics, Kish International Campus, University of Tehran, Kish, Iran
| | - Faeze Zakaryapour Sayyad
- Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran
| | - Seyyed Morteza Hashemi
- Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran
| | - Reza Shami Tanha
- Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran
| | - Mohsen Hooshmand
- Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran.
| | - Sajjad Gharaghani
- Laboratory of Bioinformatics and Drug Design (LBD), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
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Zong N, Chowdhury S, Zhou S, Rajaganapathy S, yu Y, Wang L, Dai Q, Bielinski SJ, Chen Y, Cerhan JR. Artificial Intelligence-based Efficacy Prediction of Phase 3 Clinical Trial for Repurposing Heart Failure Therapies. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.25.23290531. [PMID: 37398384 PMCID: PMC10312819 DOI: 10.1101/2023.05.25.23290531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Introduction Drug repurposing involves finding new therapeutic uses for already approved drugs, which can save costs as their pharmacokinetics and pharmacodynamics are already known. Predicting efficacy based on clinical endpoints is valuable for designing phase 3 trials and making Go/No-Go decisions, given the potential for confounding effects in phase 2. Objectives This study aims to predict the efficacy of the repurposed Heart Failure (HF) drugs for the Phase 3 Clinical Trial. Methods Our study presents a comprehensive framework for predicting drug efficacy in phase 3 trials, which combines drug-target prediction using biomedical knowledgebases with statistical analysis of real-world data. We developed a novel drug-target prediction model that uses low-dimensional representations of drug chemical structures and gene sequences, and biomedical knowledgebase. Furthermore, we conducted statistical analyses of electronic health records to assess the effectiveness of repurposed drugs in relation to clinical measurements (e.g., NT-proBNP). Results We identified 24 repurposed drugs (9 with a positive effect and 15 with a non-positive) for heart failure from 266 phase 3 clinical trials. We used 25 genes related to heart failure for drug-target prediction, as well as electronic health records (EHR) from the Mayo Clinic for screening, which contained over 58,000 heart failure patients treated with various drugs and categorized by heart failure subtypes. Our proposed drug-target predictive model performed exceptionally well in all seven tests in the BETA benchmark compared to the six cutting-edge baseline methods (i.e., best performed in 266 out of 404 tasks). For the overall prediction of the 24 drugs, our model achieved an AUCROC of 82.59% and PRAUC (average precision) of 73.39%. Conclusion The study demonstrated exceptional results in predicting the efficacy of repurposed drugs for phase 3 clinical trials, highlighting the potential of this method to facilitate computational drug repurposing.
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Affiliation(s)
- Nansu Zong
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA
| | - Shaika Chowdhury
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA
| | - Shibo Zhou
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA
| | - Sivaraman Rajaganapathy
- Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA
| | - Yue yu
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Qiying Dai
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | | | - Yongbin Chen
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, USA
| | - James R. Cerhan
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
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Amiri Souri E, Chenoweth A, Karagiannis SN, Tsoka S. Drug repurposing and prediction of multiple interaction types via graph embedding. BMC Bioinformatics 2023; 24:202. [PMID: 37193964 DOI: 10.1186/s12859-023-05317-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 04/30/2023] [Indexed: 05/18/2023] Open
Abstract
BACKGROUND Finding drugs that can interact with a specific target to induce a desired therapeutic outcome is key deliverable in drug discovery for targeted treatment. Therefore, both identifying new drug-target links, as well as delineating the type of drug interaction, are important in drug repurposing studies. RESULTS A computational drug repurposing approach was proposed to predict novel drug-target interactions (DTIs), as well as to predict the type of interaction induced. The methodology is based on mining a heterogeneous graph that integrates drug-drug and protein-protein similarity networks, together with verified drug-disease and protein-disease associations. In order to extract appropriate features, the three-layer heterogeneous graph was mapped to low dimensional vectors using node embedding principles. The DTI prediction problem was formulated as a multi-label, multi-class classification task, aiming to determine drug modes of action. DTIs were defined by concatenating pairs of drug and target vectors extracted from graph embedding, which were used as input to classification via gradient boosted trees, where a model is trained to predict the type of interaction. After validating the prediction ability of DT2Vec+, a comprehensive analysis of all unknown DTIs was conducted to predict the degree and type of interaction. Finally, the model was applied to propose potential approved drugs to target cancer-specific biomarkers. CONCLUSION DT2Vec+ showed promising results in predicting type of DTI, which was achieved via integrating and mapping triplet drug-target-disease association graphs into low-dimensional dense vectors. To our knowledge, this is the first approach that addresses prediction between drugs and targets across six interaction types.
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Affiliation(s)
- E Amiri Souri
- Department of Informatics, Faculty of Natural, Mathematical and Engineering Sciences, King's College London, Bush House, London, WC2B 4BG, UK
| | - A Chenoweth
- St. John's Institute of Dermatology, School of Basic and Medical Biosciences, Guy's Hospital, King's College London, London, SE1 9RT, UK
- Breast Cancer Now Research Unit, School of Cancer and Pharmaceutical Sciences, Guy's Cancer Centre, King's College London, London, SE1 9RT, UK
| | - S N Karagiannis
- St. John's Institute of Dermatology, School of Basic and Medical Biosciences, Guy's Hospital, King's College London, London, SE1 9RT, UK
- Breast Cancer Now Research Unit, School of Cancer and Pharmaceutical Sciences, Guy's Cancer Centre, King's College London, London, SE1 9RT, UK
| | - S Tsoka
- Department of Informatics, Faculty of Natural, Mathematical and Engineering Sciences, King's College London, Bush House, London, WC2B 4BG, UK.
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Zhou M, Sun J, Yu Z, Wu Z, Li W, Liu G, Ma L, Wang R, Tang Y. Investigation of Anti-Alzheimer's Mechanisms of Sarsasapogenin Derivatives by Network-Based Combining Structure-Based Methods. J Chem Inf Model 2023; 63:2881-2894. [PMID: 37104820 DOI: 10.1021/acs.jcim.3c00018] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
Alzheimer's disease (AD), a neurodegenerative disease with no cure, affects millions of people worldwide and has become one of the biggest healthcare challenges. Some investigated compounds play anti-AD roles at the cellular or the animal level, but their molecular mechanisms remain unclear. In this study, we designed a strategy combining network-based and structure-based methods together to identify targets for anti-AD sarsasapogenin derivatives (AAs). First, we collected drug-target interactions (DTIs) data from public databases, constructed a global DTI network, and generated drug-substructure associations. After network construction, network-based models were built for DTI prediction. The best bSDTNBI-FCFP_4 model was further used to predict DTIs for AAs. Second, a structure-based molecular docking method was employed for rescreening the prediction results to obtain more credible target proteins. Finally, in vitro experiments were conducted for validation of the predicted targets, and Nrf2 showed significant evidence as the target of anti-AD compound AA13. Moreover, we analyzed the potential mechanisms of AA13 for the treatment of AD. Generally, our combined strategy could be applied to other novel drugs or compounds and become a useful tool in identification of new targets and elucidation of disease mechanisms. Our model was deployed on our NetInfer web server (http://lmmd.ecust.edu.cn/netinfer/).
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Affiliation(s)
- Moran Zhou
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Jiamin Sun
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Zhuohang Yu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Zengrui Wu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Lei Ma
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Rui Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
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25
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Dong W, Yang Q, Wang J, Xu L, Li X, Luo G, Gao X. Multi-modality attribute learning-based method for drug-protein interaction prediction based on deep neural network. Brief Bioinform 2023; 24:7145903. [PMID: 37114624 DOI: 10.1093/bib/bbad161] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 03/19/2023] [Accepted: 04/02/2023] [Indexed: 04/29/2023] Open
Abstract
Identification of active candidate compounds for target proteins, also called drug-protein interaction (DPI) prediction, is an essential but time-consuming and expensive step, which leads to fostering the development of drug discovery. In recent years, deep network-based learning methods were frequently proposed in DPIs due to their powerful capability of feature representation. However, the performance of existing DPI methods is still limited by insufficiently labeled pharmacological data and neglected intermolecular information. Therefore, overcoming these difficulties to perfect the performance of DPIs is an urgent challenge for researchers. In this article, we designed an innovative 'multi-modality attributes' learning-based framework for DPIs with molecular transformer and graph convolutional networks, termed, multi-modality attributes (MMA)-DPI. Specifically, intermolecular sub-structural information and chemical semantic representations were extracted through an augmented transformer module from biomedical data. A tri-layer graph convolutional neural network module was applied to associate the neighbor topology information and learn the condensed dimensional features by aggregating a heterogeneous network that contains multiple biological representations of drugs, proteins, diseases and side effects. Then, the learned representations were taken as the input of a fully connected neural network module to further integrate them in molecular and topological space. Finally, the attribute representations were fused with adaptive learning weights to calculate the interaction score for the DPIs tasks. MMA-DPI was evaluated in different experimental conditions and the results demonstrate that the proposed method achieved higher performance than existing state-of-the-art frameworks.
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Affiliation(s)
- Weihe Dong
- College of information and Computer Engineering, Northeast Forestry University, Hexing Road, 150040, Harbin, China
| | - Qiang Yang
- School of Computer Science and Technology, Heilongjiang University, Xuefu Road, 150080, Harbin, China
- Postdoctoral Program of Heilongjiang Hengxun Technology Co., Ltd., Xuefu Road, 150080, Harbin, China
| | - Jian Wang
- College of information and Computer Engineering, Northeast Forestry University, Hexing Road, 150040, Harbin, China
| | - Long Xu
- School of Computer Science and Technology, Heilongjiang University, Xuefu Road, 150080, Harbin, China
- Postdoctoral Program of Heilongjiang Hengxun Technology Co., Ltd., Xuefu Road, 150080, Harbin, China
| | - Xiaokun Li
- School of Computer Science and Technology, Heilongjiang University, Xuefu Road, 150080, Harbin, China
- Postdoctoral Program of Heilongjiang Hengxun Technology Co., Ltd., Xuefu Road, 150080, Harbin, China
| | - Gongning Luo
- Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 KAUST, Thuwal 23955, Saudi Arabia
- School of Computer Science and Technology, Harbin Institute of Technology, West Dazhi Street, 150001, Harbin, China
| | - Xin Gao
- Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 KAUST, Thuwal 23955, Saudi Arabia
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26
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Yousefi N, Yazdani-Jahromi M, Tayebi A, Kolanthai E, Neal CJ, Banerjee T, Gosai A, Balasubramanian G, Seal S, Ozmen Garibay O. BindingSite-AugmentedDTA: enabling a next-generation pipeline for interpretable prediction models in drug repurposing. Brief Bioinform 2023; 24:7140297. [PMID: 37096593 DOI: 10.1093/bib/bbad136] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 03/02/2022] [Accepted: 03/16/2023] [Indexed: 04/26/2023] Open
Abstract
While research into drug-target interaction (DTI) prediction is fairly mature, generalizability and interpretability are not always addressed in the existing works in this field. In this paper, we propose a deep learning (DL)-based framework, called BindingSite-AugmentedDTA, which improves drug-target affinity (DTA) predictions by reducing the search space of potential-binding sites of the protein, thus making the binding affinity prediction more efficient and accurate. Our BindingSite-AugmentedDTA is highly generalizable as it can be integrated with any DL-based regression model, while it significantly improves their prediction performance. Also, unlike many existing models, our model is highly interpretable due to its architecture and self-attention mechanism, which can provide a deeper understanding of its underlying prediction mechanism by mapping attention weights back to protein-binding sites. The computational results confirm that our framework can enhance the prediction performance of seven state-of-the-art DTA prediction algorithms in terms of four widely used evaluation metrics, including concordance index, mean squared error, modified squared correlation coefficient ($r^2_m$) and the area under the precision curve. We also contribute to three benchmark drug-traget interaction datasets by including additional information on 3D structure of all proteins contained in those datasets, which include the two most commonly used datasets, namely Kiba and Davis, as well as the data from IDG-DREAM drug-kinase binding prediction challenge. Furthermore, we experimentally validate the practical potential of our proposed framework through in-lab experiments. The relatively high agreement between computationally predicted and experimentally observed binding interactions supports the potential of our framework as the next-generation pipeline for prediction models in drug repurposing.
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Affiliation(s)
- Niloofar Yousefi
- Industrial Engineering and Management Systems, University of Central Florida, 32816, 4000 Central Florida Blvd., Orlando, FL, USA
| | - Mehdi Yazdani-Jahromi
- Computer Science, University of Central Florida, 32816, 4000 Central Florida Blvd., Orlando, FL, USA
| | - Aida Tayebi
- Industrial Engineering and Management Systems, University of Central Florida, 32816, 4000 Central Florida Blvd., Orlando, FL, USA
| | - Elayaraja Kolanthai
- College of Medicine, Bionix Cluster, University of Central Florida, 4000 Central Florida Blvd., Orlando 32816, FL, USA
| | - Craig J Neal
- College of Medicine, Bionix Cluster, University of Central Florida, 4000 Central Florida Blvd., Orlando 32816, FL, USA
| | - Tanumoy Banerjee
- Department of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem 18015, PA, USA
| | | | - Ganesh Balasubramanian
- Department of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem 18015, PA, USA
| | - Sudipta Seal
- College of Medicine, Bionix Cluster, University of Central Florida, 4000 Central Florida Blvd., Orlando 32816, FL, USA
- Advanced Materials Processing and Analysis Center, Department of Materials Science and Engineering, University of Central Florida, 4000 Central Florida Blvd., Orlando 32816, FL, USA
| | - Ozlem Ozmen Garibay
- Industrial Engineering and Management Systems, University of Central Florida, 32816, 4000 Central Florida Blvd., Orlando, FL, USA
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27
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Yang SQ, Zhang LX, Ge YJ, Zhang JW, Hu JX, Shen CY, Lu AP, Hou TJ, Cao DS. In-silico target prediction by ensemble chemogenomic model based on multi-scale information of chemical structures and protein sequences. J Cheminform 2023; 15:48. [PMID: 37088813 PMCID: PMC10123967 DOI: 10.1186/s13321-023-00720-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 04/08/2023] [Indexed: 04/25/2023] Open
Abstract
Identification and validation of bioactive small-molecule targets is a significant challenge in drug discovery. In recent years, various in-silico approaches have been proposed to expedite time- and resource-consuming experiments for target detection. Herein, we developed several chemogenomic models for target prediction based on multi-scale information of chemical structures and protein sequences. By combining the information of a compound with multiple protein targets together and putting these compound-target pairs into a well-established model, the scores to indicate whether there are interactions between compounds and targets can be derived, and thus a target prediction task can be completed by sorting the outputted scores. To improve the prediction performance, we constructed several chemogenomic models using multi-scale information of chemical structures and protein sequences, and the ensemble model with the best performance was used as our final model. The model was validated by various strategies and external datasets and the promising target prediction capability of the model, i.e., the fraction of known targets identified in the top-k (1 to 10) list of the potential target candidates suggested by the model, was confirmed. Compared with multiple state-of-art target prediction methods, our model showed equivalent or better predictive ability in terms of the top-k predictions. It is expected that our method can be utilized as a powerful computational tool to narrow down the potential targets for experimental testing.
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Affiliation(s)
- Su-Qing Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, Hunan, People's Republic of China
- Department of Pharmacy, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, 330006, Jiangxi, People's Republic of China
| | - Liu-Xia Zhang
- The First Hospital of Hunan University of Chinese Medicine, Changsha, 410007, Hunan, People's Republic of China
| | - You-Jin Ge
- Department of Pharmacy, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, 330006, Jiangxi, People's Republic of China
| | - Jin-Wei Zhang
- Departments of Biomedical Engineering and Pathology, School of Basic Medical Science, Central South University, Changsha, 410013, Hunan, People's Republic of China
| | - Jian-Xin Hu
- Department of Pharmacy, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, 330006, Jiangxi, People's Republic of China
| | - Cheng-Ying Shen
- Department of Pharmacy, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, 330006, Jiangxi, People's Republic of China
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, People's Republic of China
| | - Ting-Jun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, People's Republic of China.
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, Hunan, People's Republic of China.
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, People's Republic of China.
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28
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Rajput A, Sevalkar G, Pardeshi K, Pingale P. COMPUTATIONAL NANOSCIENCE AND TECHNOLOGY. OPENNANO 2023. [DOI: 10.1016/j.onano.2023.100147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
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29
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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: 3.0] [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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Zhao Q, Duan G, Yang M, Cheng Z, Li Y, Wang J. AttentionDTA: Drug-Target Binding Affinity Prediction by Sequence-Based Deep Learning With Attention Mechanism. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:852-863. [PMID: 35471889 DOI: 10.1109/tcbb.2022.3170365] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The identification of drug-target relations (DTRs) is substantial in drug development. A large number of methods treat DTRs as drug-target interactions (DTIs), a binary classification problem. The main drawback of these methods are the lack of reliable negative samples and the absence of many important aspects of DTR, including their dose dependence and quantitative affinities. With increasing number of publications of drug-protein binding affinity data recently, DTRs prediction can be viewed as a regression problem of drug-target affinities (DTAs) which reflects how tightly the drug binds to the target and can present more detailed and specific information than DTIs. The growth of affinity data enables the use of deep learning architectures, which have been shown to be among the state-of-the-art methods in binding affinity prediction. Although relatively effective, due to the black-box nature of deep learning, these models are less biologically interpretable. In this study, we proposed a deep learning-based model, named AttentionDTA, which uses attention mechanism to predict DTAs. Different from the models using 3D structures of drug-target complexes or graph representation of drugs and proteins, the novelty of our work is to use attention mechanism to focus on key subsequences which are important in drug and protein sequences when predicting its affinity. We use two separate one-dimensional Convolution Neural Networks (1D-CNNs) to extract the semantic information of drug's SMILES string and protein's amino acid sequence. Furthermore, a two-side multi-head attention mechanism is developed and embedded to our model to explore the relationship between drug features and protein features. We evaluate our model on three established DTA benchmark datasets, Davis, Metz, and KIBA. AttentionDTA outperforms the state-of-the-art deep learning methods under different evaluation metrics. The results show that the attention-based model can effectively extract protein features related to drug information and drug features related to protein information to better predict drug target affinities. It is worth mentioning that we test our model on IC50 dataset, which provides the binding sites between drugs and proteins, to evaluate the ability of our model to locate binding sites. Finally, we visualize the attention weight to demonstrate the biological significance of the model. The source code of AttentionDTA can be downloaded from https://github.com/zhaoqichang/AttentionDTA_TCBB.
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DRaW: prediction of COVID-19 antivirals by deep learning-an objection on using matrix factorization. BMC Bioinformatics 2023; 24:52. [PMID: 36793010 PMCID: PMC9931173 DOI: 10.1186/s12859-023-05181-8] [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: 11/04/2022] [Accepted: 02/09/2023] [Indexed: 02/17/2023] Open
Abstract
BACKGROUND Due to the high resource consumption of introducing a new drug, drug repurposing plays an essential role in drug discovery. To do this, researchers examine the current drug-target interaction (DTI) to predict new interactions for the approved drugs. Matrix factorization methods have much attention and utilization in DTIs. However, they suffer from some drawbacks. METHODS We explain why matrix factorization is not the best for DTI prediction. Then, we propose a deep learning model (DRaW) to predict DTIs without having input data leakage. We compare our model with several matrix factorization methods and a deep model on three COVID-19 datasets. In addition, to ensure the validation of DRaW, we evaluate it on benchmark datasets. Furthermore, as an external validation, we conduct a docking study on the COVID-19 recommended drugs. RESULTS In all cases, the results confirm that DRaW outperforms matrix factorization and deep models. The docking results approve the top-ranked recommended drugs for COVID-19. CONCLUSIONS In this paper, we show that it may not be the best choice to use matrix factorization in the DTI prediction. Matrix factorization methods suffer from some intrinsic issues, e.g., sparsity in the domain of bioinformatics applications and fixed-unchanged size of the matrix-related paradigm. Therefore, we propose an alternative method (DRaW) that uses feature vectors rather than matrix factorization and demonstrates better performance than other famous methods on three COVID-19 and four benchmark datasets.
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Hu L, Fu C, Ren Z, Cai Y, Yang J, Xu S, Xu W, Tang D. SSELM-neg: spherical search-based extreme learning machine for drug-target interaction prediction. BMC Bioinformatics 2023; 24:38. [PMID: 36737694 PMCID: PMC9896467 DOI: 10.1186/s12859-023-05153-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 01/18/2023] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND The experimental verification of a drug discovery process is expensive and time-consuming. Therefore, efficiently and effectively identifying drug-target interactions (DTIs) has been the focus of research. At present, many machine learning algorithms are used for predicting DTIs. The key idea is to train the classifier using an existing DTI to predict a new or unknown DTI. However, there are various challenges, such as class imbalance and the parameter optimization of many classifiers, that need to be solved before an optimal DTI model is developed. METHODS In this study, we propose a framework called SSELM-neg for DTI prediction, in which we use a screening approach to choose high-quality negative samples and a spherical search approach to optimize the parameters of the extreme learning machine. RESULTS The results demonstrated that the proposed technique outperformed other state-of-the-art methods in 10-fold cross-validation experiments in terms of the area under the receiver operating characteristic curve (0.986, 0.993, 0.988, and 0.969) and AUPR (0.982, 0.991, 0.982, and 0.946) for the enzyme dataset, G-protein coupled receptor dataset, ion channel dataset, and nuclear receptor dataset, respectively. CONCLUSION The screening approach produced high-quality negative samples with the same number of positive samples, which solved the class imbalance problem. We optimized an extreme learning machine using a spherical search approach to identify DTIs. Therefore, our models performed better than other state-of-the-art methods.
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Affiliation(s)
- Lingzhi Hu
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China
| | - Chengzhou Fu
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China ,Guangdong Province Precise Medicine Big Data of Traditional Chinese Medicine Engineering Technology Research Center, Guangzhou, People’s Republic of China
| | - Zhonglu Ren
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China
| | - Yongming Cai
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China ,Guangdong Province Precise Medicine Big Data of Traditional Chinese Medicine Engineering Technology Research Center, Guangzhou, People’s Republic of China
| | - Jin Yang
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China ,Guangdong Province Precise Medicine Big Data of Traditional Chinese Medicine Engineering Technology Research Center, Guangzhou, People’s Republic of China
| | - Siwen Xu
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China
| | - Wenhua Xu
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China
| | - Deyu Tang
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China ,grid.79703.3a0000 0004 1764 3838School of Computer Science and Engineering, South China University of Technology, Guangzhou, People’s Republic of China ,Guangdong Province Precise Medicine Big Data of Traditional Chinese Medicine Engineering Technology Research Center, Guangzhou, People’s Republic of China
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Li G, Zhang P, Sun W, Xu J, Hu L, Zhang W. GA-ENs: A novel drug-target interactions prediction method by incorporating prior Knowledge Graph into dual Wasserstein Generative Adversarial Network with gradient penalty. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.110151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2023]
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Ren ZH, You ZH, Zou Q, Yu CQ, Ma YF, Guan YJ, You HR, Wang XF, Pan J. DeepMPF: deep learning framework for predicting drug-target interactions based on multi-modal representation with meta-path semantic analysis. J Transl Med 2023; 21:48. [PMID: 36698208 PMCID: PMC9876420 DOI: 10.1186/s12967-023-03876-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 01/05/2023] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Drug-target interaction (DTI) prediction has become a crucial prerequisite in drug design and drug discovery. However, the traditional biological experiment is time-consuming and expensive, as there are abundant complex interactions present in the large size of genomic and chemical spaces. For alleviating this phenomenon, plenty of computational methods are conducted to effectively complement biological experiments and narrow the search spaces into a preferred candidate domain. Whereas, most of the previous approaches cannot fully consider association behavior semantic information based on several schemas to represent complex the structure of heterogeneous biological networks. Additionally, the prediction of DTI based on single modalities cannot satisfy the demand for prediction accuracy. METHODS We propose a multi-modal representation framework of 'DeepMPF' based on meta-path semantic analysis, which effectively utilizes heterogeneous information to predict DTI. Specifically, we first construct protein-drug-disease heterogeneous networks composed of three entities. Then the feature information is obtained under three views, containing sequence modality, heterogeneous structure modality and similarity modality. We proposed six representative schemas of meta-path to preserve the high-order nonlinear structure and catch hidden structural information of the heterogeneous network. Finally, DeepMPF generates highly representative comprehensive feature descriptors and calculates the probability of interaction through joint learning. RESULTS To evaluate the predictive performance of DeepMPF, comparison experiments are conducted on four gold datasets. Our method can obtain competitive performance in all datasets. We also explore the influence of the different feature embedding dimensions, learning strategies and classification methods. Meaningfully, the drug repositioning experiments on COVID-19 and HIV demonstrate DeepMPF can be applied to solve problems in reality and help drug discovery. The further analysis of molecular docking experiments enhances the credibility of the drug candidates predicted by DeepMPF. CONCLUSIONS All the results demonstrate the effectively predictive capability of DeepMPF for drug-target interactions. It can be utilized as a useful tool to prescreen the most potential drug candidates for the protein. The web server of the DeepMPF predictor is freely available at http://120.77.11.78/DeepMPF/ , which can help relevant researchers to further study.
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Affiliation(s)
- Zhong-Hao Ren
- grid.460132.20000 0004 1758 0275School of Information Engineering, Xijing University, Xi’an, 710100 China
| | - Zhu-Hong You
- grid.440588.50000 0001 0307 1240School of Computer Science, Northwestern Polytechnical University, Xi’an, 710129 China
| | - Quan Zou
- grid.54549.390000 0004 0369 4060Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054 China
| | - Chang-Qing Yu
- grid.460132.20000 0004 1758 0275School of Information Engineering, Xijing University, Xi’an, 710100 China
| | - Yan-Fang Ma
- grid.417234.70000 0004 1808 3203Department of Galactophore, The Third People’s Hospital of Gansu Province, Lanzhou, 730020 China
| | - Yong-Jian Guan
- grid.460132.20000 0004 1758 0275School of Information Engineering, Xijing University, Xi’an, 710100 China
| | - Hai-Ru You
- grid.440588.50000 0001 0307 1240School of Computer Science, Northwestern Polytechnical University, Xi’an, 710129 China
| | - Xin-Fei Wang
- grid.460132.20000 0004 1758 0275School of Information Engineering, Xijing University, Xi’an, 710100 China
| | - Jie Pan
- grid.460132.20000 0004 1758 0275School of Information Engineering, Xijing University, Xi’an, 710100 China
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Suhartono D, Majiid MRN, Handoyo AT, Wicaksono P, Lucky H. Towards a more general drug target interaction prediction model using transfer learning. PROCEDIA COMPUTER SCIENCE 2023; 216:370-376. [PMID: 36643181 PMCID: PMC9829421 DOI: 10.1016/j.procs.2022.12.148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The topic of Drug-Target Interaction (DTI) topic has emerged nowadays since the COVID-19 outbreaks. DTI is one of the stages of finding a new cure for a recent disease. It determines whether a chemical compound would affect a particular protein, known as binding affinity. Recently, significant efforts have been devoted to artificial intelligence (AI) powered DTI. However, the use of transfer learning in DTI has not been explored extensively. This paper aims to make a more general DTI model by investigating DTI prediction method using Transfer learning. Three popular models will be tested and observed: CNN, RNN, and Transformer. Those models combined in several scenarios involving two extensive public datasets on DTI (BindingDB and DAVIS) to find the most optimum architecture. In our finding, combining the CNN model and BindingDB as the source data became the most recommended pre-trained model for real DTI cases. This conclusion was proved with the 6% AUPRC increase after fine-tuning the BindingDB pre-trained model to DAVIS dataset than without pre-training the model first.
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Affiliation(s)
- Derwin Suhartono
- Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia 11480
| | - Muhammad Rizki Nur Majiid
- Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia 11480
| | - Alif Tri Handoyo
- Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia 11480
| | - Pandu Wicaksono
- Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia 11480
| | - Henry Lucky
- Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia 11480
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Vora DS, Kalakoti Y, Sundar D. Computational Methods and Deep Learning for Elucidating Protein Interaction Networks. Methods Mol Biol 2023; 2553:285-323. [PMID: 36227550 DOI: 10.1007/978-1-0716-2617-7_15] [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] [Indexed: 06/16/2023]
Abstract
Protein interactions play a critical role in all biological processes, but experimental identification of protein interactions is a time- and resource-intensive process. The advances in next-generation sequencing and multi-omics technologies have greatly benefited large-scale predictions of protein interactions using machine learning methods. A wide range of tools have been developed to predict protein-protein, protein-nucleic acid, and protein-drug interactions. Here, we discuss the applications, methods, and challenges faced when employing the various prediction methods. We also briefly describe ways to overcome the challenges and prospective future developments in the field of protein interaction biology.
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Affiliation(s)
- Dhvani Sandip Vora
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India
| | - Yogesh Kalakoti
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India
| | - Durai Sundar
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India.
- School of Artificial Intelligence, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India.
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Zhou F, Yin MM, Zhao JX, Shang J, Liu JX. A Method Based On Dual-Network Information Fusion to Predict MiRNA-Disease Associations. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:52-60. [PMID: 34882558 DOI: 10.1109/tcbb.2021.3133006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
MicroRNAs (miRNAs) are single-stranded small RNAs. An increasing number of studies have shown that miRNAs play a vital role in many important biological processes. However, some experimental methods to predict unknown miRNA-disease associations (MDAs) are time-consuming and costly. Only a small percentage of MDAs are verified by researchers. Therefore, there is a great need for high-speed and efficient methods to predict novel MDAs. In this paper, a new computational method based on Dual-Network Information Fusion (DNIF) is developed to predict potential MDAs. Specifically, on the one hand, two enhanced sub-models are integrated to reconstruct an effective prediction framework; on the other hand, the prediction performance of the algorithm is improved by fully fusing multiple omics data information, including validated miRNA-disease associations network, miRNA functional similarity, disease semantic similarity and Gaussian interaction profile (GIP) kernel network associations. As a result, DNIF achieves the excellent performance under situation of 5-fold cross validation (average AUC of 0.9571). In the cases study of three important human diseases, our model has achieved satisfactory performance in predicting potential miRNAs for certain diseases. The reliable experimental results demonstrate that DNIF could serve as an effective calculation method to accelerate the identification of MDAs.
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MFIDMA: A Multiple Information Integration Model for the Prediction of Drug-miRNA Associations. BIOLOGY 2022; 12:biology12010041. [PMID: 36671734 PMCID: PMC9855084 DOI: 10.3390/biology12010041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 12/19/2022] [Accepted: 12/22/2022] [Indexed: 12/28/2022]
Abstract
Abnormal microRNA (miRNA) functions play significant roles in various pathological processes. Thus, predicting drug-miRNA associations (DMA) may hold great promise for identifying the potential targets of drugs. However, discovering the associations between drugs and miRNAs through wet experiments is time-consuming and laborious. Therefore, it is significant to develop computational prediction methods to improve the efficiency of identifying DMA on a large scale. In this paper, a multiple features integration model (MFIDMA) is proposed to predict drug-miRNA association. Specifically, we first formulated known DMA as a bipartite graph and utilized structural deep network embedding (SDNE) to learn the topological features from the graph. Second, the Word2vec algorithm was utilized to construct the attribute features of the miRNAs and drugs. Third, two kinds of features were entered into the convolution neural network (CNN) and deep neural network (DNN) to integrate features and predict potential target miRNAs for the drugs. To evaluate the MFIDMA model, it was implemented on three different datasets under a five-fold cross-validation and achieved average AUCs of 0.9407, 0.9444 and 0.8919. In addition, the MFIDMA model showed reliable results in the case studies of Verapamil and hsa-let-7c-5p, confirming that the proposed model can also predict DMA in real-world situations. The model was effective in analyzing the neighbors and topological features of the drug-miRNA network by SDNE. The experimental results indicated that the MFIDMA is an accurate and robust model for predicting potential DMA, which is significant for miRNA therapeutics research and drug discovery.
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39
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Du XX, Liu Y, Wang B, Zhang JF. lncRNA-disease association prediction method based on the nearest neighbor matrix completion model. Sci Rep 2022; 12:21653. [PMID: 36522410 PMCID: PMC9755128 DOI: 10.1038/s41598-022-25730-0] [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: 08/20/2022] [Accepted: 12/05/2022] [Indexed: 12/23/2022] Open
Abstract
State-of-the-art medical studies proved that long noncoding ribonucleic acids (lncRNAs) are closely related to various diseases. However, their large-scale detection in biological experiments is problematic and expensive. To aid screening and improve the efficiency of biological experiments, this study introduced a prediction model based on the nearest neighbor concept for lncRNA-disease association prediction. We used a new similarity algorithm in the model that fused potential associations. The experimental validation of the proposed algorithm proved its superiority over the available Cosine, Pearson, and Jaccard similarity algorithms. Satisfactory results in the comparative leave-one-out cross-validation test (with AUC = 0.96) confirmed its excellent predictive performance. Finally, the proposed model's reliability was confirmed by performing predictions using a new dataset, yielding AUC = 0.92.
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Affiliation(s)
- Xiao-xin Du
- grid.412616.60000 0001 0002 2355College of Computer and Control, Qiqihar University, Qiqihar, 161006 China
| | - Yan Liu
- grid.412616.60000 0001 0002 2355College of Computer and Control, Qiqihar University, Qiqihar, 161006 China
| | - Bo Wang
- grid.412616.60000 0001 0002 2355College of Computer and Control, Qiqihar University, Qiqihar, 161006 China
| | - Jian-fei Zhang
- grid.412616.60000 0001 0002 2355College of Computer and Control, Qiqihar University, Qiqihar, 161006 China
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Kurata H, Tsukiyama S. ICAN: Interpretable cross-attention network for identifying drug and target protein interactions. PLoS One 2022; 17:e0276609. [PMID: 36279284 PMCID: PMC9591068 DOI: 10.1371/journal.pone.0276609] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 10/10/2022] [Indexed: 11/18/2022] Open
Abstract
Drug-target protein interaction (DTI) identification is fundamental for drug discovery and drug repositioning, because therapeutic drugs act on disease-causing proteins. However, the DTI identification process often requires expensive and time-consuming tasks, including biological experiments involving large numbers of candidate compounds. Thus, a variety of computation approaches have been developed. Of the many approaches available, chemo-genomics feature-based methods have attracted considerable attention. These methods compute the feature descriptors of drugs and proteins as the input data to train machine and deep learning models to enable accurate prediction of unknown DTIs. In addition, attention-based learning methods have been proposed to identify and interpret DTI mechanisms. However, improvements are needed for enhancing prediction performance and DTI mechanism elucidation. To address these problems, we developed an attention-based method designated the interpretable cross-attention network (ICAN), which predicts DTIs using the Simplified Molecular Input Line Entry System of drugs and amino acid sequences of target proteins. We optimized the attention mechanism architecture by exploring the cross-attention or self-attention, attention layer depth, and selection of the context matrixes from the attention mechanism. We found that a plain attention mechanism that decodes drug-related protein context features without any protein-related drug context features effectively achieved high performance. The ICAN outperformed state-of-the-art methods in several metrics on the DAVIS dataset and first revealed with statistical significance that some weighted sites in the cross-attention weight matrix represent experimental binding sites, thus demonstrating the high interpretability of the results. The program is freely available at https://github.com/kuratahiroyuki/ICAN.
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Affiliation(s)
- Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan
- * E-mail:
| | - Sho Tsukiyama
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan
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41
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Zhang Y, Wu M, Wang S, Chen W. EFMSDTI: Drug-target interaction prediction based on an efficient fusion of multi-source data. Front Pharmacol 2022; 13:1009996. [PMID: 36210804 PMCID: PMC9538487 DOI: 10.3389/fphar.2022.1009996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 08/29/2022] [Indexed: 11/13/2022] Open
Abstract
Accurate identification of Drug Target Interactions (DTIs) is of great significance for understanding the mechanism of drug treatment and discovering new drugs for disease treatment. Currently, computational methods of DTIs prediction that combine drug and target multi-source data can effectively reduce the cost and time of drug development. However, in multi-source data processing, the contribution of different source data to DTIs is often not considered. Therefore, how to make full use of the contribution of different source data to predict DTIs for efficient fusion is the key to improving the prediction accuracy of DTIs. In this paper, considering the contribution of different source data to DTIs prediction, a DTIs prediction approach based on an effective fusion of drug and target multi-source data is proposed, named EFMSDTI. EFMSDTI first builds 15 similarity networks based on multi-source information networks classified as topological and semantic graphs of drugs and targets according to their biological characteristics. Then, the multi-networks are fused by selective and entropy weighting based on similarity network fusion (SNF) according to their contribution to DTIs prediction. The deep neural networks model learns the embedding of low-dimensional vectors of drugs and targets. Finally, the LightGBM algorithm based on Gradient Boosting Decision Tree (GBDT) is used to complete DTIs prediction. Experimental results show that EFMSDTI has better performance (AUROC and AUPR are 0.982) than several state-of-the-art algorithms. Also, it has a good effect on analyzing the top 1000 prediction results, while 990 of the first 1000DTIs were confirmed. Code and data are available at https://github.com/meng-jie/EFMSDTI.
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Affiliation(s)
- Yuanyuan Zhang
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao, Shandong, China
- College of Computer science and Technology, China University of Petroleum (East China), Qingdao, Shandong, China
- *Correspondence: Yuanyuan Zhang,
| | - Mengjie Wu
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao, Shandong, China
| | - Shudong Wang
- College of Computer science and Technology, China University of Petroleum (East China), Qingdao, Shandong, China
| | - Wei Chen
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao, Shandong, China
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Lin S, Shi C, Chen J. GeneralizedDTA: combining pre-training and multi-task learning to predict drug-target binding affinity for unknown drug discovery. BMC Bioinformatics 2022; 23:367. [PMID: 36071406 PMCID: PMC9449940 DOI: 10.1186/s12859-022-04905-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 08/23/2022] [Indexed: 12/04/2022] Open
Abstract
Background Accurately predicting drug-target binding affinity (DTA) in silico plays an important role in drug discovery. Most of the computational methods developed for predicting DTA use machine learning models, especially deep neural networks, and depend on large-scale labelled data. However, it is difficult to learn enough feature representation from tens of millions of compounds and hundreds of thousands of proteins only based on relatively limited labelled drug-target data. There are a large number of unknown drugs, which never appear in the labelled drug-target data. This is a kind of out-of-distribution problems in bio-medicine. Some recent studies adopted self-supervised pre-training tasks to learn structural information of amino acid sequences for enhancing the feature representation of proteins. However, the task gap between pre-training and DTA prediction brings the catastrophic forgetting problem, which hinders the full application of feature representation in DTA prediction and seriously affects the generalization capability of models for unknown drug discovery. Results To address these problems, we propose the GeneralizedDTA, which is a new DTA prediction model oriented to unknown drug discovery, by combining pre-training and multi-task learning. We introduce self-supervised protein and drug pre-training tasks to learn richer structural information from amino acid sequences of proteins and molecular graphs of drug compounds, in order to alleviate the problem of high variance caused by encoding based on deep neural networks and accelerate the convergence of prediction model on small-scale labelled data. We also develop a multi-task learning framework with a dual adaptation mechanism to narrow the task gap between pre-training and prediction for preventing overfitting and improving the generalization capability of DTA prediction model on unknown drug discovery. To validate the effectiveness of our model, we construct an unknown drug data set to simulate the scenario of unknown drug discovery. Compared with existing DTA prediction models, the experimental results show that our model has the higher generalization capability in the DTA prediction of unknown drugs. Conclusions The advantages of our model are mainly attributed to two kinds of pre-training tasks and the multi-task learning framework, which can learn richer structural information of proteins and drugs from large-scale unlabeled data, and then effectively integrate it into the downstream prediction task for obtaining a high-quality DTA prediction in unknown drug discovery.
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Affiliation(s)
- Shaofu Lin
- Faculty of Information Technology, Beijing University of Technology, No. 100, Pingleyuan, Chaoyang District, Beijing, 100124, China
| | - Chengyu Shi
- Faculty of Information Technology, Beijing University of Technology, No. 100, Pingleyuan, Chaoyang District, Beijing, 100124, China
| | - Jianhui Chen
- Faculty of Information Technology, Beijing University of Technology, No. 100, Pingleyuan, Chaoyang District, Beijing, 100124, China. .,Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing University of Technology, No. 100, Pingleyuan, Chaoyang District, Beijing, 100124, China. .,Beijing Key Laboratory of MRI and Brain Informatics, Beijing University Of Technology, No. 100, Pingleyuan, Chaoyang District, Beijing, 100124, China.
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Cheng Z, Zhao Q, Li Y, Wang J. IIFDTI: predicting drug-target interactions through interactive and independent features based on attention mechanism. Bioinformatics 2022; 38:4153-4161. [PMID: 35801934 DOI: 10.1093/bioinformatics/btac485] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 05/02/2022] [Accepted: 07/07/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Identifying drug-target interactions is a crucial step for drug discovery and design. Traditional biochemical experiments are credible to accurately validate drug-target interactions. However, they are also extremely laborious, time-consuming and expensive. With the collection of more validated biomedical data and the advancement of computing technology, the computational methods based on chemogenomics gradually attract more attention, which guide the experimental verifications. RESULTS In this study, we propose an end-to-end deep learning-based method named IIFDTI to predict drug-target interactions (DTIs) based on independent features of drug-target pairs and interactive features of their substructures. First, the interactive features of substructures between drugs and targets are extracted by the bidirectional encoder-decoder architecture. The independent features of drugs and targets are extracted by the graph neural networks and convolutional neural networks, respectively. Then, all extracted features are fused and inputted into fully connected dense layers in downstream tasks for predicting DTIs. IIFDTI takes into account the independent features of drugs/targets and simulates the interactive features of the substructures from the biological perspective. Multiple experiments show that IIFDTI outperforms the state-of-the-art methods in terms of the area under the receiver operating characteristics curve (AUC), the area under the precision-recall curve (AUPR), precision, and recall on benchmark datasets. In addition, the mapped visualizations of attention weights indicate that IIFDTI has learned the biological knowledge insights, and two case studies illustrate the capabilities of IIFDTI in practical applications. AVAILABILITY AND IMPLEMENTATION The data and codes underlying this article are available in Github at https://github.com/czjczj/IIFDTI. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zhongjian Cheng
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Qichang Zhao
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Yaohang Li
- Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA
| | - Jianxin Wang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
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Matrix factorization with denoising autoencoders for prediction of drug–target interactions. Mol Divers 2022:10.1007/s11030-022-10492-8. [DOI: 10.1007/s11030-022-10492-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Accepted: 07/01/2022] [Indexed: 11/25/2022]
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45
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Zhao Q, Yang M, Cheng Z, Li Y, Wang J. Biomedical Data and Deep Learning Computational Models for Predicting Compound-Protein Relations. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2092-2110. [PMID: 33769935 DOI: 10.1109/tcbb.2021.3069040] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The identification of compound-protein relations (CPRs), which includes compound-protein interactions (CPIs) and compound-protein affinities (CPAs), is critical to drug development. A common method for compound-protein relation identification is the use of in vitro screening experiments. However, the number of compounds and proteins is massive, and in vitro screening experiments are labor-intensive, expensive, and time-consuming with high failure rates. Researchers have developed a computational field called virtual screening (VS) to aid experimental drug development. These methods utilize experimentally validated biological interaction information to generate datasets and use the physicochemical and structural properties of compounds and target proteins as input information to train computational prediction models. At present, deep learning has been widely used in computer vision and natural language processing and has experienced epoch-making progress. At the same time, deep learning has also been used in the field of biomedicine widely, and the prediction of CPRs based on deep learning has developed rapidly and has achieved good results. The purpose of this study is to investigate and discuss the latest applications of deep learning techniques in CPR prediction. First, we describe the datasets and feature engineering (i.e., compound and protein representations and descriptors) commonly used in CPR prediction methods. Then, we review and classify recent deep learning approaches in CPR prediction. Next, a comprehensive comparison is performed to demonstrate the prediction performance of representative methods on classical datasets. Finally, we discuss the current state of the field, including the existing challenges and our proposed future directions. We believe that this investigation will provide sufficient references and insight for researchers to understand and develop new deep learning methods to enhance CPR predictions.
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Cheng Z, Yan C, Wu FX, Wang J. Drug-Target Interaction Prediction Using Multi-Head Self-Attention and Graph Attention Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2208-2218. [PMID: 33956632 DOI: 10.1109/tcbb.2021.3077905] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Identifying drug-target interactions (DTIs) is an important step in the process of new drug discovery and drug repositioning. Accurate predictions for DTIs can improve the efficiency in the drug discovery and development. Although rapid advances in deep learning technologies have generated various computational methods, it is still appealing to further investigate how to design efficient networks for predicting DTIs. In this study, we propose an end-to-end deep learning method (called MHSADTI) to predict DTIs based on the graph attention network and multi-head self-attention mechanism. First, the characteristics of drugs and proteins are extracted by the graph attention network and multi-head self-attention mechanism, respectively. Then, the attention scores are used to consider which amino acid subsequence in a protein is more important for the drug to predict its interactions. Finally, we predict DTIs by a fully connected layer after obtaining the feature vectors of drugs and proteins. MHSADTI takes advantage of self-attention mechanism for obtaining long-dependent contextual relationship in amino acid sequences and predicting DTI interpretability. More effective molecular characteristics are also obtained by the attention mechanism in graph attention networks. Multiple cross validation experiments are adopted to assess the performance of our MHSADTI. The experiments on four datasets, human, C.elegans, DUD-E and DrugBank show our method outperforms the state-of-the-art methods in terms of AUC, Precision, Recall, AUPR and F1-score. In addition, the case studies further demonstrate that our method can provide effective visualizations to interpret the prediction results from biological insights.
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Wang S, Li J, Wang Y, Juan L. A Neighborhood-Based Global Network Model to Predict Drug-Target Interactions. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2017-2025. [PMID: 33687846 DOI: 10.1109/tcbb.2021.3064614] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The detection of drug-target interactions (DTIs) plays an important role in drug discovery and development, making DTI prediction urgent to be solved. Existing computational methods usually utilize drug similarity, target similarity and DTI information to make prediction, providing the convenience of fast time and low cost. However, they usually learn features for drugs and targets separately, lacking of a global consideration. In this study, we proposed a novel neighborhood-based global network model, named as NGN, to accurately predict DTIs from the global perspective. We designed a distance constraint for features of all entities (drugs and targets) in the latent space to ensure the close distance between adjacent entities, and defined a global probability matrix to compute the predicted DTI scores on our constructed neighborhood-based global network. Results showed that NGN obtained advantageous performance compared with other state-of-the-art methods, especially surpassing them by 4.2-9.1 percent on AUPR values in the biggest dataset. Furthermore, several novel high-ranked DTIs were successfully predicted with confirmations by public sources, demonstrating the effectiveness of our method.
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DTIP-TC2A: An analytical framework for drug-target interactions prediction methods. Comput Biol Chem 2022; 99:107707. [DOI: 10.1016/j.compbiolchem.2022.107707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 05/01/2022] [Accepted: 05/26/2022] [Indexed: 11/18/2022]
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Yu N, Liu ZP, Gao R. Predicting multiple types of MicroRNA-disease associations based on tensor factorization and label propagation. Comput Biol Med 2022; 146:105558. [PMID: 35525071 DOI: 10.1016/j.compbiomed.2022.105558] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 04/06/2022] [Accepted: 04/22/2022] [Indexed: 12/29/2022]
Abstract
MicroRNAs (miRNAs) play important regulatory roles in the pathogenesis and progression of diseases. Most existing bioinformatics methods only study miRNA-disease binary association prediction. However, there are many types of associations between miRNA and disease. In addition, the miRNA-disease-type association dataset has inherent noise and incompleteness. In this paper, a novel method based on tensor factorization and label propagation (TFLP) is proposed to alleviate the above problems. First, as an effective tensor factorization method, tensor robust principal component analysis (TRPCA) is applied to the original multiple-type miRNA-disease associations to obtain a clean and complete low-rank prediction tensor. Second, the Gaussian interaction profile (GIP) kernel is used to describe the similarity of disease pairs and the similarity of miRNA pairs. Then, they are combined with disease semantic similarity and miRNA functional similarity to obtain an integrated disease similarity network and an integrated miRNA similarity network, respectively. Finally, the low-rank association tensor and the biological similarity as auxiliary information are introduced into label propagation. The prediction performance of the algorithm is improved by iterative propagation of labeled information to unlabeled samples. Extensive experiments reveal that the proposed TFLP method outperforms other state-of-the-art methods for predicting multiple types of miRNA-disease associations. The data and source codes are available at https://github.com/nayu0419/TFLP.
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Affiliation(s)
- Na Yu
- School of Control Science and Engineering, Shandong University, Jinan, 250061, China.
| | - Zhi-Ping Liu
- School of Control Science and Engineering, Shandong University, Jinan, 250061, China.
| | - Rui Gao
- School of Control Science and Engineering, Shandong University, Jinan, 250061, China.
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Alshahrani M, Almansour A, Alkhaldi A, Thafar MA, Uludag M, Essack M, Hoehndorf R. Combining biomedical knowledge graphs and text to improve predictions for drug-target interactions and drug-indications. PeerJ 2022; 10:e13061. [PMID: 35402106 PMCID: PMC8988936 DOI: 10.7717/peerj.13061] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 02/13/2022] [Indexed: 01/11/2023] Open
Abstract
Biomedical knowledge is represented in structured databases and published in biomedical literature, and different computational approaches have been developed to exploit each type of information in predictive models. However, the information in structured databases and literature is often complementary. We developed a machine learning method that combines information from literature and databases to predict drug targets and indications. To effectively utilize information in published literature, we integrate knowledge graphs and published literature using named entity recognition and normalization before applying a machine learning model that utilizes the combination of graph and literature. We then use supervised machine learning to show the effects of combining features from biomedical knowledge and published literature on the prediction of drug targets and drug indications. We demonstrate that our approach using datasets for drug-target interactions and drug indications is scalable to large graphs and can be used to improve the ranking of targets and indications by exploiting features from either structure or unstructured information alone.
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Affiliation(s)
- Mona Alshahrani
- National Center for Artificial Intelligence (NCAI), Saudi Data and Artificial Intelligence Authority (SDAIA), Riyadh, Saudi Arabia
| | - Abdullah Almansour
- National Center for Artificial Intelligence (NCAI), Saudi Data and Artificial Intelligence Authority (SDAIA), Riyadh, Saudi Arabia
| | - Asma Alkhaldi
- National Center for Artificial Intelligence (NCAI), Saudi Data and Artificial Intelligence Authority (SDAIA), Riyadh, Saudi Arabia
| | - Maha A. Thafar
- College of Computers and Information Technology, Taif University, Taif, Saudi Arabia,Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Mahmut Uludag
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Magbubah Essack
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Robert Hoehndorf
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
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