1
|
Zeng X, Zhong KY, Meng PY, Li SJ, Lv SQ, Wen ML, Li Y. MvGraphDTA: multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs. BMC Biol 2024; 22:182. [PMID: 39183297 DOI: 10.1186/s12915-024-01981-3] [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: 05/23/2024] [Accepted: 08/13/2024] [Indexed: 08/27/2024] Open
Abstract
BACKGROUND Accurately identifying drug-target affinity (DTA) plays a pivotal role in drug screening, design, and repurposing in pharmaceutical industry. It not only reduces the time, labor, and economic costs associated with biological experiments but also expedites drug development process. However, achieving the desired level of computational accuracy for DTA identification methods remains a significant challenge. RESULTS We proposed a novel multi-view-based graph deep model known as MvGraphDTA for DTA prediction. MvGraphDTA employed a graph convolutional network (GCN) to extract the structural features from original graphs of drugs and targets, respectively. It went a step further by constructing line graphs with edges as vertices based on original graphs of drugs and targets. GCN was also used to extract the relationship features within their line graphs. To enhance the complementarity between the extracted features from original graphs and line graphs, MvGraphDTA fused the extracted multi-view features of drugs and targets, respectively. Finally, these fused features were concatenated and passed through a fully connected (FC) network to predict DTA. CONCLUSIONS During the experiments, we performed data augmentation on all the training sets used. Experimental results showed that MvGraphDTA outperformed the competitive state-of-the-art methods on benchmark datasets for DTA prediction. Additionally, we evaluated the universality and generalization performance of MvGraphDTA on additional datasets. Experimental outcomes revealed that MvGraphDTA exhibited good universality and generalization capability, making it a reliable tool for drug-target interaction prediction.
Collapse
Affiliation(s)
- Xin Zeng
- College of Mathematics and Computer Science, Dali University, Dali, 671003, China
| | - Kai-Yang Zhong
- College of Mathematics and Computer Science, Dali University, Dali, 671003, China
| | - Pei-Yan Meng
- College of Mathematics and Computer Science, Dali University, Dali, 671003, China
| | - Shu-Juan Li
- Yunnan Institute of Endemic Diseases Control & Prevention, Dali, 671000, China
| | - Shuang-Qing Lv
- Institute of Surveying and Information Engineering, West Yunnan University of Applied Science, Dali, 671000, China
| | - Meng-Liang Wen
- State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Yunnan University, Kunming, 650000, China
| | - Yi Li
- College of Mathematics and Computer Science, Dali University, Dali, 671003, China.
| |
Collapse
|
2
|
Jia ZC, Yang X, Wu YK, Li M, Das D, Chen MX, Wu J. The Art of Finding the Right Drug Target: Emerging Methods and Strategies. Pharmacol Rev 2024; 76:896-914. [PMID: 38866560 PMCID: PMC11334170 DOI: 10.1124/pharmrev.123.001028] [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: 08/21/2023] [Revised: 05/28/2024] [Accepted: 05/31/2024] [Indexed: 06/14/2024] Open
Abstract
Drug targets are specific molecules in biological tissues and body fluids that interact with drugs. Drug target discovery is a key component of drug discovery and is essential for the development of new drugs in areas such as cancer therapy and precision medicine. Traditional in vitro or in vivo target discovery methods are time-consuming and labor-intensive, limiting the pace of drug discovery. With the development of modern discovery methods, the discovery and application of various emerging technologies have greatly improved the efficiency of drug discovery, shortened the cycle time, and reduced the cost. This review provides a comprehensive overview of various emerging drug target discovery strategies, including computer-assisted approaches, drug affinity response target stability, multiomics analysis, gene editing, and nonsense-mediated mRNA degradation, and discusses the effectiveness and limitations of the various approaches, as well as their application in real cases. Through the review of the aforementioned contents, a general overview of the development of novel drug targets and disease treatment strategies will be provided, and a theoretical basis will be provided for those who are engaged in pharmaceutical science research. SIGNIFICANCE STATEMENT: Target-based drug discovery has been the main approach to drug discovery in the pharmaceutical industry for the past three decades. Traditional drug target discovery methods based on in vivo or in vitro validation are time-consuming and costly, greatly limiting the development of new drugs. Therefore, the development and selection of new methods in the drug target discovery process is crucial.
Collapse
Affiliation(s)
- Zi-Chang Jia
- State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals of Guizhou University, Guiyang, China (Z.-C.J., X.Y., Y.-K.W., M.-X.C., J.W.); The Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee (D.D.); and State Key Laboratory of Crop Biology, College of Life Science, Shandong Agricultural University, Taian, Shandong, China (M.L.)
| | - Xue Yang
- State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals of Guizhou University, Guiyang, China (Z.-C.J., X.Y., Y.-K.W., M.-X.C., J.W.); The Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee (D.D.); and State Key Laboratory of Crop Biology, College of Life Science, Shandong Agricultural University, Taian, Shandong, China (M.L.)
| | - Yi-Kun Wu
- State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals of Guizhou University, Guiyang, China (Z.-C.J., X.Y., Y.-K.W., M.-X.C., J.W.); The Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee (D.D.); and State Key Laboratory of Crop Biology, College of Life Science, Shandong Agricultural University, Taian, Shandong, China (M.L.)
| | - Min Li
- State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals of Guizhou University, Guiyang, China (Z.-C.J., X.Y., Y.-K.W., M.-X.C., J.W.); The Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee (D.D.); and State Key Laboratory of Crop Biology, College of Life Science, Shandong Agricultural University, Taian, Shandong, China (M.L.)
| | - Debatosh Das
- State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals of Guizhou University, Guiyang, China (Z.-C.J., X.Y., Y.-K.W., M.-X.C., J.W.); The Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee (D.D.); and State Key Laboratory of Crop Biology, College of Life Science, Shandong Agricultural University, Taian, Shandong, China (M.L.) ;
| | - Mo-Xian Chen
- State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals of Guizhou University, Guiyang, China (Z.-C.J., X.Y., Y.-K.W., M.-X.C., J.W.); The Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee (D.D.); and State Key Laboratory of Crop Biology, College of Life Science, Shandong Agricultural University, Taian, Shandong, China (M.L.) ;
| | - Jian Wu
- State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals of Guizhou University, Guiyang, China (Z.-C.J., X.Y., Y.-K.W., M.-X.C., J.W.); The Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee (D.D.); and State Key Laboratory of Crop Biology, College of Life Science, Shandong Agricultural University, Taian, Shandong, China (M.L.) ;
| |
Collapse
|
3
|
Kalemati M, Zamani Emani M, Koohi S. DCGAN-DTA: Predicting drug-target binding affinity with deep convolutional generative adversarial networks. BMC Genomics 2024; 25:411. [PMID: 38724911 PMCID: PMC11080241 DOI: 10.1186/s12864-024-10326-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 04/19/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND In recent years, there has been a growing interest in utilizing computational approaches to predict drug-target binding affinity, aiming to expedite the early drug discovery process. To address the limitations of experimental methods, such as cost and time, several machine learning-based techniques have been developed. However, these methods encounter certain challenges, including the limited availability of training data, reliance on human intervention for feature selection and engineering, and a lack of validation approaches for robust evaluation in real-life applications. RESULTS To mitigate these limitations, in this study, we propose a method for drug-target binding affinity prediction based on deep convolutional generative adversarial networks. Additionally, we conducted a series of validation experiments and implemented adversarial control experiments using straw models. These experiments serve to demonstrate the robustness and efficacy of our predictive models. We conducted a comprehensive evaluation of our method by comparing it to baselines and state-of-the-art methods. Two recently updated datasets, namely the BindingDB and PDBBind, were used for this purpose. Our findings indicate that our method outperforms the alternative methods in terms of three performance measures when using warm-start data splitting settings. Moreover, when considering physiochemical-based cold-start data splitting settings, our method demonstrates superior predictive performance, particularly in terms of the concordance index. CONCLUSION The results of our study affirm the practical value of our method and its superiority over alternative approaches in predicting drug-target binding affinity across multiple validation sets. This highlights the potential of our approach in accelerating drug repurposing efforts, facilitating novel drug discovery, and ultimately enhancing disease treatment. The data and source code for this study were deposited in the GitHub repository, https://github.com/mojtabaze7/DCGAN-DTA . Furthermore, the web server for our method is accessible at https://dcgan.shinyapps.io/bindingaffinity/ .
Collapse
Affiliation(s)
- Mahmood Kalemati
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
| | - Mojtaba Zamani Emani
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
| | - Somayyeh Koohi
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.
| |
Collapse
|
4
|
Zeng X, Li SJ, Lv SQ, Wen ML, Li Y. A comprehensive review of the recent advances on predicting drug-target affinity based on deep learning. Front Pharmacol 2024; 15:1375522. [PMID: 38628639 PMCID: PMC11019008 DOI: 10.3389/fphar.2024.1375522] [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: 01/24/2024] [Accepted: 03/21/2024] [Indexed: 04/19/2024] Open
Abstract
Accurate calculation of drug-target affinity (DTA) is crucial for various applications in the pharmaceutical industry, including drug screening, design, and repurposing. However, traditional machine learning methods for calculating DTA often lack accuracy, posing a significant challenge in accurately predicting DTA. Fortunately, deep learning has emerged as a promising approach in computational biology, leading to the development of various deep learning-based methods for DTA prediction. To support researchers in developing novel and highly precision methods, we have provided a comprehensive review of recent advances in predicting DTA using deep learning. We firstly conducted a statistical analysis of commonly used public datasets, providing essential information and introducing the used fields of these datasets. We further explored the common representations of sequences and structures of drugs and targets. These analyses served as the foundation for constructing DTA prediction methods based on deep learning. Next, we focused on explaining how deep learning models, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer, and Graph Neural Networks (GNNs), were effectively employed in specific DTA prediction methods. We highlighted the unique advantages and applications of these models in the context of DTA prediction. Finally, we conducted a performance analysis of multiple state-of-the-art methods for predicting DTA based on deep learning. The comprehensive review aimed to help researchers understand the shortcomings and advantages of existing methods, and further develop high-precision DTA prediction tool to promote the development of drug discovery.
Collapse
Affiliation(s)
- Xin Zeng
- College of Mathematics and Computer Science, Dali University, Dali, China
| | - Shu-Juan Li
- Yunnan Institute of Endemic Diseases Control and Prevention, Dali, China
| | - Shuang-Qing Lv
- Institute of Surveying and Information Engineering West Yunnan University of Applied Science, Dali, China
| | - Meng-Liang Wen
- State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Yunnan University, Kunming, China
| | - Yi Li
- College of Mathematics and Computer Science, Dali University, Dali, China
| |
Collapse
|
5
|
Huang Z, Xiao Q, Xiong T, Shi W, Yang Y, Li G. Predicting Drug-Protein Interactions through Branch-Chain Mining and multi-dimensional attention network. Comput Biol Med 2024; 171:108127. [PMID: 38350397 DOI: 10.1016/j.compbiomed.2024.108127] [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: 10/24/2023] [Revised: 01/26/2024] [Accepted: 02/06/2024] [Indexed: 02/15/2024]
Abstract
Identifying drug-protein interactions (DPIs) is crucial in drug discovery and repurposing. Computational methods for precise DPI identification can expedite development timelines and reduce expenses compared with conventional experimental methods. Lately, deep learning techniques have been employed for predicting DPIs, enhancing these processes. Nevertheless, the limitations observed in prior studies, where many extract features from complete drug and protein entities, overlooking the crucial theoretical foundation that pharmacological responses are often correlated with specific substructures, can lead to poor predictive performance. Furthermore, certain substructure-focused research confines its exploration to a solitary fragment category, such as a functional group. In this study, addressing these constraints, we present an end-to-end framework termed BCMMDA for predicting DPIs. The framework considers various substructure types, including branch chains, common substructures, and specific fragments. We designed a specific feature learning module by combining our proposed multi-dimensional attention mechanism with convolutional neural networks (CNNs). Deep CNNs assist in capturing the synergistic effects among these fragment sets, enabling the extraction of relevant features of drugs and proteins. Meanwhile, the multi-dimensional attention mechanism refines the relationship between drug and protein features by assigning attention vectors to each drug compound and amino acid. This mechanism empowers the model to further concentrate on pivotal substructures and elements, thereby improving its ability to identify essential interactions in DPI prediction. We evaluated the performance of BCMMDA on four well-known benchmark datasets. The results indicated that BCMMDA outperformed state-of-the-art baseline models, demonstrating significant improvement in performance.
Collapse
Affiliation(s)
- Zhuo Huang
- College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China
| | - Qiu Xiao
- College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China; MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, 410081, China; College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China.
| | - Tuo Xiong
- College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China
| | - Wanwan Shi
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China
| | - Yide Yang
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, 410006, China.
| | - Guanghui Li
- School of Information Engineering, East China Jiaotong University, Nanchang, 330013, China.
| |
Collapse
|
6
|
Qi H, Yu T, Yu W, Liu C. Drug-target affinity prediction with extended graph learning-convolutional networks. BMC Bioinformatics 2024; 25:75. [PMID: 38365583 PMCID: PMC10874073 DOI: 10.1186/s12859-024-05698-6] [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: 01/15/2024] [Accepted: 02/12/2024] [Indexed: 02/18/2024] Open
Abstract
BACKGROUND High-performance computing plays a pivotal role in computer-aided drug design, a field that holds significant promise in pharmaceutical research. The prediction of drug-target affinity (DTA) is a crucial stage in this process, potentially accelerating drug development through rapid and extensive preliminary compound screening, while also minimizing resource utilization and costs. Recently, the incorporation of deep learning into DTA prediction and the enhancement of its accuracy have emerged as key areas of interest in the research community. Drugs and targets can be characterized through various methods, including structure-based, sequence-based, and graph-based representations. Despite the progress in structure and sequence-based techniques, they tend to provide limited feature information. Conversely, graph-based approaches have risen to prominence, attracting considerable attention for their comprehensive data representation capabilities. Recent studies have focused on constructing protein and drug molecular graphs using sequences and SMILES, subsequently deriving representations through graph neural networks. However, these graph-based approaches are limited by the use of a fixed adjacent matrix of protein and drug molecular graphs for graph convolution. This limitation restricts the learning of comprehensive feature representations from intricate compound and protein structures, consequently impeding the full potential of graph-based feature representation in DTA prediction. This, in turn, significantly impacts the models' generalization capabilities in the complex realm of drug discovery. RESULTS To tackle these challenges, we introduce GLCN-DTA, a model specifically designed for proficiency in DTA tasks. GLCN-DTA innovatively integrates a graph learning module into the existing graph architecture. This module is designed to learn a soft adjacent matrix, which effectively and efficiently refines the contextual structure of protein and drug molecular graphs. This advancement allows for learning richer structural information from protein and drug molecular graphs via graph convolution, specifically tailored for DTA tasks, compared to the conventional fixed adjacent matrix approach. A series of experiments have been conducted to validate the efficacy of the proposed GLCN-DTA method across diverse scenarios. The results demonstrate that GLCN-DTA possesses advantages in terms of robustness and high accuracy. CONCLUSIONS The proposed GLCN-DTA model enhances DTA prediction performance by introducing a novel framework that synergizes graph learning operations with graph convolution operations, thereby achieving richer representations. GLCN-DTA does not distinguish between different protein classifications, including structurally ordered and intrinsically disordered proteins, focusing instead on improving feature representation. Therefore, its applicability scope may be more effective in scenarios involving structurally ordered proteins, while potentially being limited in contexts with intrinsically disordered proteins.
Collapse
Affiliation(s)
- Haiou Qi
- Nursing Department, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China
| | - Ting Yu
- Operating Room Department, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China.
| | - Wenwen Yu
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Chenxi Liu
- School of Medicine and Health Management, Tongji Medical School, Huazhong University of Science and Technology, Wuhan, 430030, China
| |
Collapse
|
7
|
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.
Collapse
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.
| |
Collapse
|