1
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Zhang Y, Deng Z, Xu X, Feng Y, Junliang S. Application of Artificial Intelligence in Drug-Drug Interactions Prediction: A Review. J Chem Inf Model 2024; 64:2158-2173. [PMID: 37458400 DOI: 10.1021/acs.jcim.3c00582] [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] [Indexed: 04/09/2024]
Abstract
Drug-drug interactions (DDI) are a critical aspect of drug research that can have adverse effects on patients and can lead to serious consequences. Predicting these events accurately can significantly improve clinicians' ability to make better decisions and establish optimal treatment regimens. However, manually detecting these interactions is time-consuming and labor-intensive. Utilizing the advancements in Artificial Intelligence (AI) is essential for achieving accurate forecasts of DDIs. In this review, DDI prediction tasks are classified into three types according to the type of DDI prediction: undirected DDI prediction, DDI events prediction, and Asymmetric DDI prediction. The paper then reviews the progress of AI for each of these three prediction tasks in DDI and provides a summary of the data sets used as well as the representative methods used in these three prediction directions. In this review, we aim to provide a comprehensive overview of drug interaction prediction. The first section introduces commonly used databases and presents an overview of current research advancements and techniques across three domains of DDI. Additionally, we introduce classical machine learning techniques for predicting undirected drug interactions and provide a timeline for the progression of the predicted drug interaction events. At last, we debate the difficulties and prospects of AI approaches at predicting DDI, emphasizing their potential for improving clinical decision-making and patient outcomes.
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Affiliation(s)
- Yuanyuan Zhang
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao,266000,China
| | - Zengqian Deng
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao,266000,China
| | - Xiaoyu Xu
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao,266000,China
| | - Yinfei Feng
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao,266000,China
| | - Shang Junliang
- School of Information Science and Engineering, Qufu Normal University, Rizhao, 276800, China
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2
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Asfand-E-Yar M, Hashir Q, Shah AA, Malik HAM, Alourani A, Khalil W. Multimodal CNN-DDI: using multimodal CNN for drug to drug interaction associated events. Sci Rep 2024; 14:4076. [PMID: 38374325 PMCID: PMC10876630 DOI: 10.1038/s41598-024-54409-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 02/12/2024] [Indexed: 02/21/2024] Open
Abstract
Drug-to-drug interaction (DDIs) occurs when a patient consumes multiple drugs. Therefore, it is possible that any medication can influence other drugs' effectiveness. The drug-to-drug interactions are detected based on the interactions of chemical substructures, targets, pathways, and enzymes; therefore, machine learning (ML) and deep learning (DL) techniques are used to find the associated DDI events. The DL model, i.e., Convolutional Neural Network (CNN), is used to analyze the DDI. DDI is based on the 65 different drug-associated events, which is present in the drug bank database. Our model uses the inputs, which are chemical structures (i.e., smiles of drugs), enzymes, pathways, and the target of the drug. Therefore, for the multi-model CNN, we use several layers, activation functions, and features of drugs to achieve better accuracy as compared to traditional prediction algorithms. We perform different experiments on various hyperparameters. We have also carried out experiments on various iterations of drug features in different sets. Our Multi-Modal Convolutional Neural Network - Drug to Drug Interaction (MCNN-DDI) model achieved an accuracy of 90.00% and an AUPR of 94.78%. The results showed that a combination of the drug's features (i.e., chemical substructure, target, and enzyme) performs better in DDIs-associated events prediction than other features.
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Affiliation(s)
- Muhammad Asfand-E-Yar
- Department of Computer Science, CoE-AI, Center of Excellence Artificial Intelligence, Bahria University, Islamabad, Pakistan
| | - Qadeer Hashir
- Department of Computer Science, CoE-AI, Center of Excellence Artificial Intelligence, Bahria University, Islamabad, Pakistan
| | - Asghar Ali Shah
- Department of Computer Science, Bahria University, Islamabad , Pakistan
| | | | - Abdullah Alourani
- Department of Management Information Systems and Production Management, College of Business and Economics, Qassim University, Buraydah 51452, Saudi Arabia.
| | - Waqar Khalil
- Department of Computer Science, CoE-AI, Center of Excellence Artificial Intelligence, Bahria University, Islamabad, Pakistan
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3
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Pan D, Lu P, Wu Y, Kang L, Huang F, Lin K, Yang F. Prediction of multiple types of drug interactions based on multi-scale fusion and dual-view fusion. Front Pharmacol 2024; 15:1354540. [PMID: 38434701 PMCID: PMC10904638 DOI: 10.3389/fphar.2024.1354540] [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: 12/14/2023] [Accepted: 01/30/2024] [Indexed: 03/05/2024] Open
Abstract
Potential drug-drug interactions (DDI) can lead to adverse drug reactions (ADR), and DDI prediction can help pharmacy researchers detect harmful DDI early. However, existing DDI prediction methods fall short in fully capturing drug information. They typically employ a single-view input, focusing solely on drug features or drug networks. Moreover, they rely exclusively on the final model layer for predictions, overlooking the nuanced information present across various network layers. To address these limitations, we propose a multi-scale dual-view fusion (MSDF) method for DDI prediction. More specifically, MSDF first constructs two views, topological and feature views of drugs, as model inputs. Then a graph convolutional neural network is used to extract the feature representations from each view. On top of that, a multi-scale fusion module integrates information across different graph convolutional layers to create comprehensive drug embeddings. The embeddings from the two views are summed as the final representation for classification. Experiments on two real-world datasets demonstrate that MSDF achieves higher accuracy than state-of-the-art methods, as the dual-view, multi-scale approach better captures drug characteristics.
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Affiliation(s)
- Dawei Pan
- School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China
| | - Ping Lu
- School of Economics and Management, Xiamen University of Technology, Xiamen, China
| | - Yunbing Wu
- College of Computer and Big Data, Fuzhou University, Fuzhou, China
| | - Liping Kang
- Pasteur Institute, Soochow University, Suzhou, China
| | - Fengxin Huang
- School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China
| | - Kaibiao Lin
- School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China
| | - Fan Yang
- Shenzhen Research Institute of Xiamen University, Shenzhen, China
- Department of Automation, Xiamen University, Xiamen, China
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4
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Liang Z, Lin C, Tan G, Li J, He Y, Cai S. A low-cost machine learning framework for predicting drug-drug interactions based on fusion of multiple features and a parameter self-tuning strategy. Phys Chem Chem Phys 2024; 26:6300-6315. [PMID: 38305788 DOI: 10.1039/d4cp00039k] [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: 02/03/2024]
Abstract
Poly-drug therapy is now recognized as a crucial treatment, and the analysis of drug-drug interactions (DDIs) offers substantial theoretical support and guidance for its implementation. Predicting potential DDIs using intelligent algorithms is an emerging approach in pharmacological research. However, the existing supervised models and deep learning-based techniques still have several limitations. This paper proposes a novel DDI analysis and prediction framework called the Multi-View Semi-supervised Graph-based (MVSG) framework, which provides a comprehensive judgment by integrating multiple DDI features and functions without any time-consuming training process. Unlike conventional approaches, MVSG can search for the most suitable similarity (or distance) measurement among DDI data and construct graph structures for each feature. By employing a parameter self-tuning strategy, MVSG fuses multiple graphs according to the contributions of features' information. The actual anticancer drug data are extracted from the authoritative public database for evaluating the effectiveness of our framework, including 904 drugs, 7730 DDI records and 19 types of drug interactions. Validation results indicate that the prediction is more accurate when multiple features are adopted by our framework. In comparison to conventional machine learning techniques, MVSG can achieve higher performance even with less labeled data and without a training process. Finally, MVSG is employed to narrow down the search for potential valuable combinations.
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Affiliation(s)
- Zexiao Liang
- School of Integrated Circuits, Guangdong University of Technology, 100 Waihuan Xi Road, Panyu District, Guangzhou, 510006, Guangdong, China.
| | - Canxin Lin
- School of Computer Science and Technology, Guangdong University of Technology, 100 Waihuan Xi Road, Panyu District, Guangzhou, 510006, Guangdong, China
| | - Guoliang Tan
- School of Automation, Guangdong University of Technology, 100 Waihuan Xi Road, Panyu District, Guangzhou, 510006, Guangdong, China
| | - Jianzhong Li
- School of Integrated Circuits, Guangdong University of Technology, 100 Waihuan Xi Road, Panyu District, Guangzhou, 510006, Guangdong, China.
| | - Yan He
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuan Xi Road, Panyu District, Guangzhou, 510006, Guangdong, China
| | - Shuting Cai
- School of Integrated Circuits, Guangdong University of Technology, 100 Waihuan Xi Road, Panyu District, Guangzhou, 510006, Guangdong, China.
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5
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Deng Z, Xu J, Feng Y, Dong L, Zhang Y. MAVGAE: a multimodal framework for predicting asymmetric drug-drug interactions based on variational graph autoencoder. Comput Methods Biomech Biomed Engin 2024:1-13. [PMID: 38314513 DOI: 10.1080/10255842.2024.2311315] [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: 10/31/2023] [Accepted: 01/21/2024] [Indexed: 02/06/2024]
Abstract
Drug-drug interactions refer to the phenomena wherein the potency, duration, or effectiveness of one or multiple drugs undergo alterations of varying degrees as a result of their concurrent or sequential usage. The accurate identification of potential drug interactions plays a pivotal role in mitigating the risks associated with drug administration in patients, it also helps in minimizing the likelihood of hazardous situations arising during a patient's course of treatment. However, researchers have found that there is a problem of asymmetric drug interactions, where one drug may affect another but not vice versa. This adds to the difficulty of prediction, so in polypharmacy, the order of drug administration is critical to efficacy and safety, and few current studies predict asymmetric DDIs. Aiming at the above problems, we propose a framework based on multimodal data and a variational graph autoencoder named MAVGAE for predicting asymmetric drug interactions. The framework initially encodes multimodal data into low-dimensional representations and then utilizes a variational graph autoencoder for encoding and decoding. During the model training process, supervised learning is employed for the classification task with the incorporation of heterogeneity information, ensuring accurate prediction of drug interactions. Experimental validation on a large-scale drug dataset demonstrates the framework's high accuracy and reliability in predicting non-symmetrical drug interactions, offering effective support and guidance for drug research.
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Affiliation(s)
- Zengqian Deng
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao, China
| | - Jie Xu
- School of Information Science and Engineering, Qufu Normal University, Rizhao, China
| | - Yinfei Feng
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao, China
| | - Liangcheng Dong
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao, China
| | - Yuanyuan Zhang
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao, China
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6
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Wang NN, Zhu B, Li XL, Liu S, Shi JY, Cao DS. Comprehensive Review of Drug-Drug Interaction Prediction Based on Machine Learning: Current Status, Challenges, and Opportunities. J Chem Inf Model 2024; 64:96-109. [PMID: 38132638 DOI: 10.1021/acs.jcim.3c01304] [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: 12/23/2023]
Abstract
Detecting drug-drug interactions (DDIs) is an essential step in drug development and drug administration. Given the shortcomings of current experimental methods, the machine learning (ML) approach has become a reliable alternative, attracting extensive attention from the academic and industrial fields. With the rapid development of computational science and the growing popularity of cross-disciplinary research, a large number of DDI prediction studies based on ML methods have been published in recent years. To give an insight into the current situation and future direction of DDI prediction research, we systemically review these studies from three aspects: (1) the classic DDI databases, mainly including databases of drugs, side effects, and DDI information; (2) commonly used drug attributes, which focus on chemical, biological, and phenotypic attributes for representing drugs; (3) popular ML approaches, such as shallow learning-based, deep learning-based, recommender system-based, and knowledge graph-based methods for DDI detection. For each section, related studies are described, summarized, and compared, respectively. In the end, we conclude the research status of DDI prediction based on ML methods and point out the existing issues, future challenges, potential opportunities, and subsequent research direction.
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Affiliation(s)
- Ning-Ning Wang
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China
| | - Bei Zhu
- School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, Shanxi, P.R. China
| | - Xin-Liang Li
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China
| | - Shao Liu
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China
| | - Jian-Yu Shi
- School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, Shanxi, P.R. China
| | - Dong-Sheng Cao
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P.R. China
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7
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Lin S, Mao X, Hong L, Lin S, Wei DQ, Xiong Y. MATT-DDI: Predicting multi-type drug-drug interactions via heterogeneous attention mechanisms. Methods 2023; 220:1-10. [PMID: 37858611 DOI: 10.1016/j.ymeth.2023.10.007] [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: 09/22/2023] [Revised: 10/13/2023] [Accepted: 10/17/2023] [Indexed: 10/21/2023] Open
Abstract
The joint use of multiple drugs can result in adverse drug-drug interactions (DDIs) and side effects that harm the body. Accurate identification of DDIs is crucial for avoiding accidental drug side effects and understanding potential mechanisms underlying DDIs. Several computational methods have been proposed for multi-type DDI prediction, but most rely on the similarity profiles of drugs as the drug feature vectors, which may result in information leakage and overoptimistic performance when predicting interactions between new drugs. To address this issue, we propose a novel method, MATT-DDI, for predicting multi-type DDIs based on the original feature vectors of drugs and multiple attention mechanisms. MATT-DDI consists of three main modules: the top k most similar drug pair selection module, heterogeneous attention mechanism module and multi‑type DDI prediction module. Firstly, based on the feature vector of the input drug pair (IDP), k drug pairs that are most similar to the input drug pair from the training dataset are selected according to cosine similarity between drug pairs. Then, the vectors of k selected drug pairs are averaged to obtain a new drug pair (NDP). Next, IDP and NDP are fed into heterogeneous attention modules, including scaled dot product attention and bilinear attention, to extract latent feature vectors. Finally, these latent feature vectors are taken as input of the classification module to predict DDI types. We evaluated MATT-DDI on three different tasks. The experimental results show that MATT-DDI provides better or comparable performance compared to several state-of-the-art methods, and its feasibility is supported by case studies. MATT-DDI is a robust model for predicting multi-type DDIs with excellent performance and no information leakage.
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Affiliation(s)
- Shenggeng Lin
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xueying Mao
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Liang Hong
- Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China; School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Shuangjun Lin
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China; Zhongjing Research and Industrialization Institute of Chinese Medicine, Nanyang 473006, China; Peng Cheng National Laboratory, Shenzhen 518055, China
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China; Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China.
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8
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Li Z, Tu X, Chen Y, Lin W. HetDDI: a pre-trained heterogeneous graph neural network model for drug-drug interaction prediction. Brief Bioinform 2023; 24:bbad385. [PMID: 37903412 DOI: 10.1093/bib/bbad385] [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: 05/20/2023] [Revised: 08/12/2023] [Accepted: 09/13/2023] [Indexed: 11/01/2023] Open
Abstract
The simultaneous use of two or more drugs due to multi-disease comorbidity continues to increase, which may cause adverse reactions between drugs that seriously threaten public health. Therefore, the prediction of drug-drug interaction (DDI) has become a hot topic not only in clinics but also in bioinformatics. In this study, we propose a novel pre-trained heterogeneous graph neural network (HGNN) model named HetDDI, which aggregates the structural information in drug molecule graphs and rich semantic information in biomedical knowledge graph to predict DDIs. In HetDDI, we first initialize the parameters of the model with different pre-training methods. Then we apply the pre-trained HGNN to learn the feature representation of drugs from multi-source heterogeneous information, which can more effectively utilize drugs' internal structure and abundant external biomedical knowledge, thus leading to better DDI prediction. We evaluate our model on three DDI prediction tasks (binary-class, multi-class and multi-label) with three datasets and further assess its performance on three scenarios (S1, S2 and S3). The results show that the accuracy of HetDDI can achieve 98.82% in the binary-class task, 98.13% in the multi-class task and 96.66% in the multi-label one on S1, which outperforms the state-of-the-art methods by at least 2%. On S2 and S3, our method also achieves exciting performance. Furthermore, the case studies confirm that our model performs well in predicting unknown DDIs. Source codes are available at https://github.com/LinsLab/HetDDI.
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Affiliation(s)
- Zhe Li
- School of Computer Science, University of South China, Hengyang, 421001 Hunan, China
| | - Xinyi Tu
- School of Computer Science, University of South China, Hengyang, 421001 Hunan, China
| | - Yuping Chen
- School of Pharmacy, University of South China, Hengyang 421001, China
| | - Wenbin Lin
- School of Mathematics and Physics, University of South China, Hengyang 421001, China
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9
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Gan Y, Liu W, Xu G, Yan C, Zou G. DMFDDI: deep multimodal fusion for drug-drug interaction prediction. Brief Bioinform 2023; 24:bbad397. [PMID: 37930025 DOI: 10.1093/bib/bbad397] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 09/28/2023] [Accepted: 10/13/2023] [Indexed: 11/07/2023] Open
Abstract
Drug combination therapy has gradually become a promising treatment strategy for complex or co-existing diseases. As drug-drug interactions (DDIs) may cause unexpected adverse drug reactions, DDI prediction is an important task in pharmacology and clinical applications. Recently, researchers have proposed several deep learning methods to predict DDIs. However, these methods mainly exploit the chemical or biological features of drugs, which is insufficient and limits the performances of DDI prediction. Here, we propose a new deep multimodal feature fusion framework for DDI prediction, DMFDDI, which fuses drug molecular graph, DDI network and the biochemical similarity features of drugs to predict DDIs. To fully extract drug molecular structure, we introduce an attention-gated graph neural network for capturing the global features of the molecular graph and the local features of each atom. A sparse graph convolution network is introduced to learn the topological structure information of the DDI network. In the multimodal feature fusion module, an attention mechanism is used to efficiently fuse different features. To validate the performance of DMFDDI, we compare it with 10 state-of-the-art methods. The comparison results demonstrate that DMFDDI achieves better performance in DDI prediction. Our method DMFDDI is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/DHUDEBLab/DMFDDI.git.
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Affiliation(s)
- Yanglan Gan
- School of Computer Science and Technology, Donghua University, 2999 North Renmin Road, 201600, Shanghai, China
| | - Wenxiao Liu
- School of Computer Science and Technology, Donghua University, 2999 North Renmin Road, 201600, Shanghai, China
| | - Guangwei Xu
- School of Computer Science and Technology, Donghua University, 2999 North Renmin Road, 201600, Shanghai, China
| | - Cairong Yan
- School of Computer Science and Technology, Donghua University, 2999 North Renmin Road, 201600, Shanghai, China
| | - Guobing Zou
- School of Computer Engineering and Science, Shanghai University, 99 Shangda Road, 200444, Shanghai, China
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10
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MSResG: Using GAE and Residual GCN to Predict Drug-Drug Interactions Based on Multi-source Drug Features. Interdiscip Sci 2023; 15:171-188. [PMID: 36646843 DOI: 10.1007/s12539-023-00550-6] [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: 09/15/2022] [Revised: 01/05/2023] [Accepted: 01/07/2023] [Indexed: 01/18/2023]
Abstract
Drug-drug interaction refers to taking the two drugs may produce certain reaction which may be a threat to patients' health, or enhance the efficacy helpful for medical work. Therefore, it is necessary to study and predict it. In fact, traditional experimental methods can be used for drug-drug interaction prediction, but they are time-consuming and costly, so we prefer to use more accurate and convenient calculation methods to predict the unknown drug-drug interaction. In this paper, we proposed a deep learning framework called MSResG that considers multi-sources features of drugs and combines them with Graph Auto-Encoder to predicting. Firstly, the model obtains four feature representations of drugs from the database, namely, chemical substructure, target, pathway and enzyme, and then calculates the Jaccard similarity of the drugs. To balance different drug features, we perform similarity integration by finding the mean value. Then we will be comprehensive similarity network combined with drug interaction network, and encodes and decodes it using the graph auto-encoder based on residual graph convolution network. Encoding is to learn the potential feature vectors of drugs, which contain similar information and interaction information. Decoding is to reconstruct the network to predict unknown drug-drug interaction. The experimental results show that our model has advanced performance and is superior to other existing advanced methods. Case study also shows that MSResG has practical significance.
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11
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Askr H, Elgeldawi E, Aboul Ella H, Elshaier YAMM, Gomaa MM, Hassanien AE. Deep learning in drug discovery: an integrative review and future challenges. Artif Intell Rev 2022; 56:5975-6037. [PMID: 36415536 PMCID: PMC9669545 DOI: 10.1007/s10462-022-10306-1] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/24/2022] [Indexed: 11/18/2022]
Abstract
Recently, using artificial intelligence (AI) in drug discovery has received much attention since it significantly shortens the time and cost of developing new drugs. Deep learning (DL)-based approaches are increasingly being used in all stages of drug development as DL technology advances, and drug-related data grows. Therefore, this paper presents a systematic Literature review (SLR) that integrates the recent DL technologies and applications in drug discovery Including, drug-target interactions (DTIs), drug-drug similarity interactions (DDIs), drug sensitivity and responsiveness, and drug-side effect predictions. We present a review of more than 300 articles between 2000 and 2022. The benchmark data sets, the databases, and the evaluation measures are also presented. In addition, this paper provides an overview of how explainable AI (XAI) supports drug discovery problems. The drug dosing optimization and success stories are discussed as well. Finally, digital twining (DT) and open issues are suggested as future research challenges for drug discovery problems. Challenges to be addressed, future research directions are identified, and an extensive bibliography is also included.
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Affiliation(s)
- Heba Askr
- Faculty of Computers and Artificial Intelligence, University of Sadat City, Sadat City, Egypt
| | - Enas Elgeldawi
- Computer Science Department, Faculty of Science, Minia University, Minia, Egypt
| | - Heba Aboul Ella
- Faculty of Pharmacy and Drug Technology, Chinese University in Egypt (CUE), Cairo, Egypt
| | | | - Mamdouh M. Gomaa
- Computer Science Department, Faculty of Science, Minia University, Minia, Egypt
| | - Aboul Ella Hassanien
- Faculty of Computers and Artificial Intelligence, Cairo University, Cairo, Egypt
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12
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Lin K, Kang L, Yang F, Lu P, Lu J. MFDA: Multiview fusion based on dual-level attention for drug interaction prediction. Front Pharmacol 2022; 13:1021329. [PMID: 36278200 PMCID: PMC9584567 DOI: 10.3389/fphar.2022.1021329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 09/13/2022] [Indexed: 11/30/2022] Open
Abstract
Drug-drug interaction prediction plays an important role in pharmacology and clinical applications. Most traditional methods predict drug interactions based on drug attributes or network structure. They usually have three limitations: 1) failing to integrate drug features and network structures well, resulting in less informative drug embeddings; 2) being restricted to a single view of drug interaction relationships; 3) ignoring the importance of different neighbors. To tackle these challenges, this paper proposed a multiview fusion based on dual-level attention to predict drug interactions (called MFDA). The MFDA first constructed multiple views for the drug interaction relationship, and then adopted a cross-fusion strategy to deeply fuse drug features with the drug interaction network under each view. To distinguish the importance of different neighbors and views, MFDA adopted a dual-level attention mechanism (node level and view level) to obtain the unified drug embedding for drug interaction prediction. Extensive experiments were conducted on real datasets, and the MFDA demonstrated superior performance compared to state-of-the-art baselines. In the multitask analysis of new drug reactions, MFDA obtained higher scores on multiple metrics. In addition, its prediction results corresponded to specific drug reaction events, which achieved more accurate predictions.
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Affiliation(s)
- Kaibiao Lin
- School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China
| | - Liping Kang
- School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China
- *Correspondence: Liping Kang, ; Fan Yang,
| | - Fan Yang
- Shenzhen Research Institute of Xiamen University, Shenzhen, China
- Department of Automation, Xiamen University, Xiamen, China
- *Correspondence: Liping Kang, ; Fan Yang,
| | - Ping Lu
- School of Economics and Management, Xiamen University of Technology, Xiamen, China
| | - Jiangtao Lu
- School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China
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13
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Qiu Y, Zhang Y, Deng Y, Liu S, Zhang W. A Comprehensive Review of Computational Methods For Drug-Drug Interaction Detection. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1968-1985. [PMID: 34003753 DOI: 10.1109/tcbb.2021.3081268] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The detection of drug-drug interactions (DDIs) is a crucial task for drug safety surveillance, which provides effective and safe co-prescriptions of multiple drugs. Since laboratory researches are often complicated, costly and time-consuming, it's urgent to develop computational approaches to detect drug-drug interactions. In this paper, we conduct a comprehensive review of state-of-the-art computational methods falling into three categories: literature-based extraction methods, machine learning-based prediction methods and pharmacovigilance-based data mining methods. Literature-based extraction methods detect DDIs from published literature using natural language processing techniques; machine learning-based prediction methods build prediction models based on the known DDIs in databases and predict novel ones; pharmacovigilance-based data mining methods usually apply statistical techniques on various electronic data to detect drug-drug interaction signals. We first present the taxonomy of drug-drug interaction detection methods and provide the outlines of three categories of methods. Afterwards, we respectively introduce research backgrounds and data sources of three categories, and illustrate their representative approaches as well as evaluation metrics. Finally, we discuss the current challenges of existing methods and highlight potential opportunities for future directions.
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Gao J, Lyu T, Xiong F, Wang J, Ke W, Li Z. Predicting the Survival of Cancer Patients With Multimodal Graph Neural Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:699-709. [PMID: 34033545 DOI: 10.1109/tcbb.2021.3083566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
In recent years, cancer patients survival prediction holds important significance for worldwide health problems, and has gained many researchers attention in medical information communities. Cancer patients survival prediction can be seen the classification work which is a meaningful and challenging task. Nevertheless, research in this field is still limited. In this work, we design a novel Multimodal Graph Neural Network (MGNN)framework for predicting cancer survival, which explores the features of real-world multimodal data such as gene expression, copy number alteration and clinical data in a unified framework. Specifically, we first construct the bipartite graphs between patients and multimodal data to explore the inherent relation. Subsequently, the embedding of each patient on different bipartite graphs is obtained with graph neural network. Finally, a multimodal fusion neural layer is proposed to fuse the medical features from different modality data. Comprehensive experiments have been conducted on real-world datasets, which demonstrate the superiority of our modal with significant improvements against state-of-the-arts. Furthermore, the proposed MGNN is validated to be more robust on other four cancer datasets.
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ADDI: Recommending alternatives for drug-drug interactions with negative health effects. Comput Biol Med 2020; 125:103969. [PMID: 32836102 DOI: 10.1016/j.compbiomed.2020.103969] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 08/09/2020] [Accepted: 08/09/2020] [Indexed: 11/21/2022]
Abstract
Investigating the interactions among various drugs is an indispensable issue in the field of computational biology. Scientific literature represents a rich source for the retrieval of knowledge about the interactions between drugs. Predicting drug-drug interaction (DDI) types will help biologists to evade hazardous drug interactions and support them in discovering potential alternatives that increase therapeutic efficacy and reduce toxicity. In this paper, we propose a general-purpose method called ADDI (standing for Alternative Drug-Drug Interaction) that applies deep learning on PubMed abstracts to predict interaction types among drugs. As an application, ADDI recommends alternatives for drug-drug interactions (DDIs) which have Negative Health Effects Types (NHETs). ADDI clearly outperforms state-of-the-art methods, on average by 13%, with respect to accuracy by using only the textual content of the online PubMed papers. Additionally, manual evaluation of ADDI indicates high precision in recommending alternatives for DDIs with NHETs.
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