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Niu D, Zhang L, Zhang B, Zhang Q, Li Z. DAS-DDI: A dual-view framework with drug association and drug structure for drug-drug interaction prediction. J Biomed Inform 2024; 156:104672. [PMID: 38857738 DOI: 10.1016/j.jbi.2024.104672] [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: 03/18/2024] [Revised: 05/09/2024] [Accepted: 06/06/2024] [Indexed: 06/12/2024]
Abstract
In drug development and clinical application, drug-drug interaction (DDI) prediction is crucial for patient safety and therapeutic efficacy. However, traditional methods for DDI prediction often overlook the structural features of drugs and the complex interrelationships between them, which affect the accuracy and interpretability of the model. In this paper, a novel dual-view DDI prediction framework, DAS-DDI is proposed. Firstly, a drug association network is constructed based on similarity information among drugs, which could provide rich context information for DDI prediction. Subsequently, a novel drug substructure extraction method is proposed, which could update the features of nodes and chemical bonds simultaneously, improving the comprehensiveness of the feature. Furthermore, an attention mechanism is employed to fuse multiple drug embeddings from different views dynamically, enhancing the discriminative ability of the model in handling multi-view data. Comparative experiments on three public datasets demonstrate the superiority of DAS-DDI compared with other state-of-the-art models under two scenarios.
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Affiliation(s)
- Dongjiang Niu
- College of Computer Science and Technology, Qingdao University, No. 308 Ningxia Road, Qingdao, 266071, Shandong, China
| | - Lianwei Zhang
- College of Computer Science and Technology, Qingdao University, No. 308 Ningxia Road, Qingdao, 266071, Shandong, China
| | - Beiyi Zhang
- College of Computer Science and Technology, Qingdao University, No. 308 Ningxia Road, Qingdao, 266071, Shandong, China
| | - Qiang Zhang
- College of Computer Science and Technology, Qingdao University, No. 308 Ningxia Road, Qingdao, 266071, Shandong, China
| | - Zhen Li
- College of Computer Science and Technology, Qingdao University, No. 308 Ningxia Road, Qingdao, 266071, Shandong, China.
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2
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Wang G, Feng H, Cao C. BiRNN-DDI: A Drug-Drug Interaction Event Type Prediction Model Based on Bidirectional Recurrent Neural Network and Graph2Seq Representation. J Comput Biol 2024. [PMID: 39049806 DOI: 10.1089/cmb.2024.0476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2024] Open
Abstract
Research on drug-drug interaction (DDI) prediction, particularly in identifying DDI event types, is crucial for understanding adverse drug reactions and drug combinations. This work introduces a Bidirectional Recurrent Neural Network model for DDI event type prediction (BiRNN-DDI), which simultaneously considers structural relationships and contextual information. Our BiRNN-DDI model constructs drug feature graphs to mine structural relationships. For contextual information, it transforms drug graphs into sequences and employs a two-channel structure, integrating BiRNN, to obtain contextual representations of drug-drug pairs. The model's effectiveness is demonstrated through comparisons with state-of-the-art models on two DDI event-type benchmarks. Extensive experimental results reveal that BiRNN-DDI surpasses other models in accuracy, AUPR, AUC, F1 score, Precision, and Recall metrics on both small and large datasets. Additionally, our model exhibits a lower parameter space, indicating more efficient learning of drug feature representations and prediction of potential DDI event types.
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Affiliation(s)
- GuiShen Wang
- School of Computer Science and Engineering, Changchun University of Technology, Changchun, China
| | - Hui Feng
- School of Computer Science and Engineering, Changchun University of Technology, Changchun, China
| | - Chen Cao
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
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3
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Hou LX, Yi HC, You ZH, Chen SH, Zheng J, Kwoh CK. MathEagle: Accurate prediction of drug-drug interaction events via multi-head attention and heterogeneous attribute graph learning. Comput Biol Med 2024; 177:108642. [PMID: 38820777 DOI: 10.1016/j.compbiomed.2024.108642] [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: 10/10/2023] [Revised: 05/18/2024] [Accepted: 05/21/2024] [Indexed: 06/02/2024]
Abstract
BACKGROUND Drug-drug interaction events influence the effectiveness of drug combinations and can lead to unexpected side effects or exacerbate underlying diseases, jeopardizing patient prognosis. Most existing methods are restricted to predicting whether two drugs interact or the type of drug-drug interactions, while very few studies endeavor to predict the specific risk levels of side effects of drug combinations. METHODS In this study, we propose MathEagle, a novel approach to predict accurate risk levels of drug combinations based on multi-head attention and heterogeneous attribute graph learning. Initially, we model drugs and three distinct risk levels between drugs as a heterogeneous information graph. Subsequently, behavioral and chemical structure features of drugs are utilized by message passing neural networks and graph embedding algorithms, respectively. Ultimately, MathEagle employs heterogeneous graph convolution and multi-head attention mechanisms to learn efficient latent representations of drug nodes and estimates the risk levels of pairwise drugs in an end-to-end manner. RESULTS To assess the effectiveness and robustness of the model, five-fold cross-validation, ablation experiments, and case studies were conducted. MathEagle achieved an accuracy of 85.85 % and an AUC of 0.9701 on the drug risk level prediction task and is superior to all comparative models. The MathEagle predictor is freely accessible at http://120.77.11.78/MathEagle/. CONCLUSIONS The experimental results indicate that MathEagle can function as an effective tool for predicting accurate risk of drug combinations, aiding in guiding clinical medication, and enhancing patient outcomes.
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Affiliation(s)
- Lin-Xuan Hou
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710129, China; Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an, 710129, China
| | - Hai-Cheng Yi
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710129, China; Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an, 710129, China.
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710129, China.
| | - Shi-Hong Chen
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710129, China
| | - Jia Zheng
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710129, China
| | - Chee Keong Kwoh
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore
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4
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Jiang Z, Gong Z, Dai X, Zhang H, Ding P, Shen C. Deep graph contrastive learning model for drug-drug interaction prediction. PLoS One 2024; 19:e0304798. [PMID: 38885206 PMCID: PMC11182529 DOI: 10.1371/journal.pone.0304798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 05/17/2024] [Indexed: 06/20/2024] Open
Abstract
Drug-drug interaction (DDI) is the combined effects of multiple drugs taken together, which can either enhance or reduce each other's efficacy. Thus, drug interaction analysis plays an important role in improving treatment effectiveness and patient safety. It has become a new challenge to use computational methods to accelerate drug interaction time and reduce its cost-effectiveness. The existing methods often do not fully explore the relationship between the structural information and the functional information of drug molecules, resulting in low prediction accuracy for drug interactions, poor generalization, and other issues. In this paper, we propose a novel method, which is a deep graph contrastive learning model for drug-drug interaction prediction (DeepGCL for brevity). DeepGCL incorporates a contrastive learning component to enhance the consistency of information between different views (molecular structure and interaction network), which means that the DeepGCL model predicts drug interactions by integrating molecular structure features and interaction network topology features. Experimental results show that DeepGCL achieves better performance than other methods in all datasets. Moreover, we conducted many experiments to analyze the necessity of each component of the model and the robustness of the model, which also showed promising results. The source code of DeepGCL is freely available at https://github.com/jzysj/DeepGCL.
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Affiliation(s)
- Zhenyu Jiang
- College of Information and Intelligence, Hunan Agricultural University, Changsha, China
| | - Zhi Gong
- School of Computer Science and Engineering, Hunan University of Information Technology, Changsha, China
- Key Laboratory of Intelligent Perception and Computing, Hunan University of Information Technology, Changsha, China
| | - Xiaopeng Dai
- College of Information and Intelligence, Hunan Agricultural University, Changsha, China
- School of Computer Science and Engineering, Hunan University of Information Technology, Changsha, China
- Key Laboratory of Intelligent Perception and Computing, Hunan University of Information Technology, Changsha, China
| | - Hongyan Zhang
- College of Information and Intelligence, Hunan Agricultural University, Changsha, China
| | - Pingjian Ding
- School of Computer Science, University of South China, Hengyang, China
| | - Cong Shen
- School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore
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5
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Ma X, Li Z, Du Z, Xu Y, Chen Y, Zhuo L, Fu X, Liu R. Advancing cancer driver gene detection via Schur complement graph augmentation and independent subspace feature extraction. Comput Biol Med 2024; 174:108484. [PMID: 38643595 DOI: 10.1016/j.compbiomed.2024.108484] [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: 02/04/2024] [Revised: 03/18/2024] [Accepted: 04/15/2024] [Indexed: 04/23/2024]
Abstract
Accurately identifying cancer driver genes (CDGs) is crucial for guiding cancer treatment and has recently received great attention from researchers. However, the high complexity and heterogeneity of cancer gene regulatory networks limit the precition accuracy of existing deep learning models. To address this, we introduce a model called SCIS-CDG that utilizes Schur complement graph augmentation and independent subspace feature extraction techniques to effectively predict potential CDGs. Firstly, a random Schur complement strategy is adopted to generate two augmented views of gene network within a graph contrastive learning framework. Rapid randomization of the random Schur complement strategy enhances the model's generalization and its ability to handle complex networks effectively. Upholding the Schur complement principle in expectations promotes the preservation of the original gene network's vital structure in the augmented views. Subsequently, we employ feature extraction technology using multiple independent subspaces, each trained with independent weights to reduce inter-subspace dependence and improve the model's expressiveness. Concurrently, we introduced a feature expansion component based on the structure of the gene network to address issues arising from the limited dimensionality of node features. Moreover, it can alleviate the challenges posed by the heterogeneity of cancer gene networks to some extent. Finally, we integrate a learnable attention weight mechanism into the graph neural network (GNN) encoder, utilizing feature expansion technology to optimize the significance of various feature levels in the prediction task. Following extensive experimental validation, the SCIS-CDG model has exhibited high efficiency in identifying known CDGs and uncovering potential unknown CDGs in external datasets. Particularly when compared to previous conventional GNN models, its performance has seen significant improved. The code and data are publicly available at: https://github.com/mxqmxqmxq/SCIS-CDG.
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Affiliation(s)
- Xinqian Ma
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, 325027, Wenzhou, China
| | - Zhen Li
- School of Computer Science of Information Technology, Qiannan Normal University for Nationalities, Duyun, Guizhou 558000, China; Institute of Computational Science and Technology, Guangzhou University, 510000, Guangzhou, China
| | - Zhenya Du
- Guangzhou Xinhua University, 510520, Guangzhou, China
| | - Yan Xu
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, 325027, Wenzhou, China
| | - Yifan Chen
- College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, Hunan, 410004, China
| | - Linlin Zhuo
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, 325027, Wenzhou, China.
| | - Xiangzheng Fu
- College of Computer Science and Electronic Engineering, Hunan University, 410012, Changsha, China
| | - Ruijun Liu
- School of Software, Beihang University, Beijing, China.
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6
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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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7
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Luo H, Yin W, Wang J, Zhang G, Liang W, Luo J, Yan C. Drug-drug interactions prediction based on deep learning and knowledge graph: A review. iScience 2024; 27:109148. [PMID: 38405609 PMCID: PMC10884936 DOI: 10.1016/j.isci.2024.109148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2024] Open
Abstract
Drug-drug interactions (DDIs) can produce unpredictable pharmacological effects and lead to adverse events that have the potential to cause irreversible damage to the organism. Traditional methods to detect DDIs through biological or pharmacological analysis are time-consuming and expensive, therefore, there is an urgent need to develop computational methods to effectively predict drug-drug interactions. Currently, deep learning and knowledge graph techniques which can effectively extract features of entities have been widely utilized to develop DDI prediction methods. In this research, we aim to systematically review DDI prediction researches applying deep learning and graph knowledge. The available biomedical data and public databases related to drugs are firstly summarized in this review. Then, we discuss the existing drug-drug interactions prediction methods which have utilized deep learning and knowledge graph techniques and group them into three main classes: deep learning-based methods, knowledge graph-based methods, and methods that combine deep learning with knowledge graph. We comprehensively analyze the commonly used drug related data and various DDI prediction methods, and compare these prediction methods on benchmark datasets. Finally, we briefly discuss the challenges related to drug-drug interactions prediction, including asymmetric DDIs prediction and high-order DDI prediction.
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Affiliation(s)
- Huimin Luo
- School of Computer and Information Engineering, Henan University, Kaifeng, China
- Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, China
| | - Weijie Yin
- School of Computer and Information Engineering, Henan University, Kaifeng, China
| | - Jianlin Wang
- School of Computer and Information Engineering, Henan University, Kaifeng, China
- Academy for Advanced Interdisciplinary Studies, Zhengzhou, China
| | - Ge Zhang
- School of Computer and Information Engineering, Henan University, Kaifeng, China
- Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, China
| | - Wenjuan Liang
- School of Computer and Information Engineering, Henan University, Kaifeng, China
| | - Junwei Luo
- College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, China
| | - Chaokun Yan
- School of Computer and Information Engineering, Henan University, Kaifeng, China
- Academy for Advanced Interdisciplinary Studies, Zhengzhou, China
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8
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Zhu J, Che C, Jiang H, Xu J, Yin J, Zhong Z. SSF-DDI: a deep learning method utilizing drug sequence and substructure features for drug-drug interaction prediction. BMC Bioinformatics 2024; 25:39. [PMID: 38262923 PMCID: PMC10810255 DOI: 10.1186/s12859-024-05654-4] [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: 10/20/2023] [Accepted: 01/12/2024] [Indexed: 01/25/2024] Open
Abstract
BACKGROUND Drug-drug interactions (DDI) are prevalent in combination therapy, necessitating the importance of identifying and predicting potential DDI. While various artificial intelligence methods can predict and identify potential DDI, they often overlook the sequence information of drug molecules and fail to comprehensively consider the contribution of molecular substructures to DDI. RESULTS In this paper, we proposed a novel model for DDI prediction based on sequence and substructure features (SSF-DDI) to address these issues. Our model integrates drug sequence features and structural features from the drug molecule graph, providing enhanced information for DDI prediction and enabling a more comprehensive and accurate representation of drug molecules. CONCLUSION The results of experiments and case studies have demonstrated that SSF-DDI significantly outperforms state-of-the-art DDI prediction models across multiple real datasets and settings. SSF-DDI performs better in predicting DDI involving unknown drugs, resulting in a 5.67% improvement in accuracy compared to state-of-the-art methods.
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Affiliation(s)
- Jing Zhu
- Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian, 116000, China
| | - Chao Che
- School of Software Engineering, Dalian University, Dalian, 116000, China
| | - Hao Jiang
- Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian, 116000, China
| | - Jian Xu
- General Surgery, Affiliated Zhongshan Hospital of Dalian University, Dalian, 116000, China
| | - Jiajun Yin
- General Surgery, Affiliated Zhongshan Hospital of Dalian University, Dalian, 116000, China
| | - Zhaoqian Zhong
- Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian, 116000, China.
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9
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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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10
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Feng J, Liang Y, Yu T. MM-GANN-DDI: Multimodal Graph-Agnostic Neural Networks for Predicting Drug-Drug Interaction Events. Comput Biol Med 2023; 166:107492. [PMID: 37820558 DOI: 10.1016/j.compbiomed.2023.107492] [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: 05/12/2023] [Revised: 08/15/2023] [Accepted: 09/15/2023] [Indexed: 10/13/2023]
Abstract
Personalized treatment of complex diseases relies on combined medication. However, the occurrence of unexpected drug-drug interactions (DDIs) in these combinations can lead to adverse effects or even fatalities. Although recent computational methods exhibit promising performance in DDI screening, their practical implementation faces two significant challenges: (i) the availability of comprehensive datasets to support clinical application, and (ii) the ability to infer DDI types for new drugs beyond the existing dataset coverage. To mitigate these challenges, we propose MM-GANN-DDI: a Multimodal Graph-Agnostic Neural Network for Predicting Drug-Drug Interaction Events. We first mine six drug modalities and incorporate a graph attention (GAT) mechanism to fuse these modalities with the topological features of the DDI graph. We further propose a novel graph neural network training mechanism called graph-agnostic meta-training (GAMT), which effectively leverages topological information from the DDI graph and efficiently predicts DDI types for new drugs beyond the available dataset. Specifically, GAMT samples meta-graphs from the original DDI graph, splitting them into support and query sets to simulate seen and unseen drugs. Two-level optimizations are applied to enhance the model's generalization capability. We evaluate our model on two datasets (DB-v1 and DB-v2) across three tasks. Our MM-GANN-DDI demonstrates competitive performance on all three tasks. Notably, in Task 2, which focuses on predicting DDI types for drugs outside the dataset, our proposed model outperforms other methods, exhibiting an improvement of 4.6 percentage points in AUPR on DB-v1 and 5.9 percentage points on DB-v2. Additionally, our model surpasses state-of-the-art methods and classic approaches in terms of accuracy, F1 score, precision, and recall. Ablation experiments provide further validation of the effectiveness of the proposed model design. Importantly, our model exhibits the potential to discover unobserved DDIs, demonstrating its practical application in clinical medication.
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Affiliation(s)
- Junning Feng
- Faculty of Innovation Engineering, Macau University of Science and Technology, 999078, Macao Special Administrative Region of China; School of Data Science, The Chinese University of Hong Kong-Shenzhen, Shenzhen, 518055, China
| | - Yong Liang
- Peng Cheng Laboratory, Shenzhen, 518055, China.
| | - Tianwei Yu
- School of Data Science, The Chinese University of Hong Kong-Shenzhen, Shenzhen, 518055, China
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11
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Ning G, Sun Y, Ling J, Chen J, He J. BDN-DDI: A bilinear dual-view representation learning framework for drug-drug interaction prediction. Comput Biol Med 2023; 165:107340. [PMID: 37603959 DOI: 10.1016/j.compbiomed.2023.107340] [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: 06/02/2023] [Revised: 07/23/2023] [Accepted: 08/07/2023] [Indexed: 08/23/2023]
Abstract
Drug-drug interactions (DDIs) refer to the potential effects of two or more drugs interacting with each other when used simultaneously, which may lead to adverse reactions or reduced drug efficacy. Accurate prediction of DDIs is a significant concern in recent years. Currently, the drug chemical substructure-based learning method has substantially improved DDIs prediction. However, we notice that most related works ignore the detailed interaction among atoms when extracting the substructure information of drugs. This problem results in incomplete information extraction and may limit the model's predictive ability. In this work, we proposed a novel framework named BDN-DDI (a bilinear dual-view representation learning framework for drug-drug interaction prediction) to infer potential DDIs. In the proposed framework, the encoder consists of six stacked BDN blocks, each of which extracts the feature representation of drug molecules through a bilinear representation extraction layer. The extracted feature is then used to learn embeddings of drug substructures from the single drug learning layer (intra-layer) and the drug-pair learning layer (inter-layer). Finally, the learned embeddings are fed into a decoder to predict DDI events. Based on our experiments, BDN-DDI has an AUROC value of over 99% for the warm-start task. Additionally, it outperformed the state-of-the-art methods by an average of 3.4% for the cold-start tasks. Finally, our method's effectiveness is further validated by visualizing several case studies.
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Affiliation(s)
- Guoquan Ning
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China
| | - Yuping Sun
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China.
| | - Jie Ling
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China
| | - Jijia Chen
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China
| | - Jiaxi He
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China
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12
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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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13
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Zhang R, Wang X, Wang P, Meng Z, Cui W, Zhou Y. HTCL-DDI: a hierarchical triple-view contrastive learning framework for drug-drug interaction prediction. Brief Bioinform 2023; 24:bbad324. [PMID: 37742052 DOI: 10.1093/bib/bbad324] [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: 05/05/2023] [Revised: 07/26/2023] [Accepted: 08/24/2023] [Indexed: 09/25/2023] Open
Abstract
Drug-drug interaction (DDI) prediction can discover potential risks of drug combinations in advance by detecting drug pairs that are likely to interact with each other, sparking an increasing demand for computational methods of DDI prediction. However, existing computational DDI methods mostly rely on the single-view paradigm, failing to handle the complex features and intricate patterns of DDIs due to the limited expressiveness of the single view. To this end, we propose a Hierarchical Triple-view Contrastive Learning framework for Drug-Drug Interaction prediction (HTCL-DDI), leveraging the molecular, structural and semantic views to model the complicated information involved in DDI prediction. To aggregate the intra-molecular compositional and structural information, we present a dual attention-aware network in the molecular view. Based on the molecular view, to further capture inter-molecular information, we utilize the one-hop neighboring information and high-order semantic relations in the structural view and semantic view, respectively. Then, we introduce contrastive learning to enhance drug representation learning from multifaceted aspects and improve the robustness of HTCL-DDI. Finally, we conduct extensive experiments on three real-world datasets. All the experimental results show the significant improvement of HTCL-DDI over the state-of-the-art methods, which also demonstrates that HTCL-DDI opens new avenues for ensuring medication safety and identifying synergistic drug combinations.
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Affiliation(s)
- Ran Zhang
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xuezhi Wang
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Pengfei Wang
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhen Meng
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Wenjuan Cui
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yuanchun Zhou
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
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14
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Ru Z, Wu Y, Shao J, Yin J, Qian L, Miao X. A dual-modal graph learning framework for identifying interaction events among chemical and biotech drugs. Brief Bioinform 2023; 24:bbad271. [PMID: 37507113 DOI: 10.1093/bib/bbad271] [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: 03/28/2023] [Revised: 06/18/2023] [Accepted: 07/06/2023] [Indexed: 07/30/2023] Open
Abstract
Drug-drug interaction (DDI) identification is essential to clinical medicine and drug discovery. The two categories of drugs (i.e. chemical drugs and biotech drugs) differ remarkably in molecular properties, action mechanisms, etc. Biotech drugs are up-to-comers but highly promising in modern medicine due to higher specificity and fewer side effects. However, existing DDI prediction methods only consider chemical drugs of small molecules, not biotech drugs of large molecules. Here, we build a large-scale dual-modal graph database named CB-DB and customize a graph-based framework named CB-TIP to reason event-aware DDIs for both chemical and biotech drugs. CB-DB comprehensively integrates various interaction events and two heterogeneous kinds of molecular structures. It imports endogenous proteins founded on the fact that most drugs take effects by interacting with endogenous proteins. In the modality of molecular structure, drugs and endogenous proteins are two heterogeneous kinds of graphs, while in the modality of interaction, they are nodes connected by events (i.e. edges of different relationships). CB-TIP employs graph representation learning methods to generate drug representations from either modality and then contrastively mixes them to predict how likely an event occurs when a drug meets another in an end-to-end manner. Experiments demonstrate CB-TIP's great superiority in DDI prediction and the promising potential of uncovering novel DDIs.
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Affiliation(s)
- Zhongying Ru
- Center for Data Science, Zhejiang University, 866 Yuhangtang Rd, 310058, Hangzhou, P.R. China
- Polytechnic Institute, Zhejiang University, 866 Yuhangtang Rd, 310058, Hangzhou, P.R. China
| | - Yangyang Wu
- Center for Data Science, Zhejiang University, 866 Yuhangtang Rd, 310058, Hangzhou, P.R. China
| | - Jinning Shao
- Institute of Drug Metabolism and Pharmaceutical Analysis, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Cancer Center, & Hangzhou Institute of Innovative Medicine, Zhejiang University, 866 Yuhangtang Rd, 310058, Hangzhou, P.R. China
| | - Jianwei Yin
- Center for Data Science, Zhejiang University, 866 Yuhangtang Rd, 310058, Hangzhou, P.R. China
- College of Computer Science, Zhejiang University, 866 Yuhangtang Rd, 310058, Hangzhou, P.R. China
| | - Linghui Qian
- Institute of Drug Metabolism and Pharmaceutical Analysis, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Cancer Center, & Hangzhou Institute of Innovative Medicine, Zhejiang University, 866 Yuhangtang Rd, 310058, Hangzhou, P.R. China
| | - Xiaoye Miao
- Center for Data Science, Zhejiang University, 866 Yuhangtang Rd, 310058, Hangzhou, P.R. China
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15
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Ju H, Bai J, Jiang J, Che Y, Chen X. Comparative evaluation and analysis of DNA N4-methylcytosine methylation sites using deep learning. Front Genet 2023; 14:1254827. [PMID: 37671040 PMCID: PMC10476523 DOI: 10.3389/fgene.2023.1254827] [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: 07/07/2023] [Accepted: 07/31/2023] [Indexed: 09/07/2023] Open
Abstract
DNA N4-methylcytosine (4mC) is significantly involved in biological processes, such as DNA expression, repair, and replication. Therefore, accurate prediction methods are urgently needed. Deep learning methods have transformed applications that previously require sequencing expertise into engineering challenges that do not require expertise to solve. Here, we compare a variety of state-of-the-art deep learning models on six benchmark datasets to evaluate their performance in 4mC methylation site detection. We visualize the statistical analysis of the datasets and the performance of different deep-learning models. We conclude that deep learning can greatly expand the potential of methylation site prediction.
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Affiliation(s)
- Hong Ju
- Heilongjiang Agricultural Engineering Vocational College, Harbin, China
| | - Jie Bai
- Engineering Research Center of Integration and Application of Digital Learning Technology, Ministry of Education, Hangzhou, China
| | - Jing Jiang
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Yusheng Che
- Heilongjiang Agricultural Engineering Vocational College, Harbin, China
| | - Xin Chen
- Department of Neurosurgical Laboratory, The First Affiliated Hospital of Harbin Medical University, Harbin, China
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16
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Du L, Liu H, Zhang L, Lu Y, Li M, Hu Y, Zhang Y. Deep ensemble learning for accurate retinal vessel segmentation. Comput Biol Med 2023; 158:106829. [PMID: 37054633 DOI: 10.1016/j.compbiomed.2023.106829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 03/09/2023] [Accepted: 03/26/2023] [Indexed: 04/15/2023]
Abstract
Significant progress has been made in deep learning-based retinal vessel segmentation in recent years. However, the current methods suffer from low performance and the robust of the models is not that good. Our work introduces an novel framework for retinal vessel segmentation based on deep ensemble learning. The results of benchmarking comparisons indicate that our model outperforms the existing ones on multiple datasets, demonstrating that our models are more effective, superior, and robust for the retinal vessel segmentation. It evinces the capability of our model to capture the discriminative feature representations through introducing the ensemble strategy to integrate different base deep learning models like pyramid vision Transformer and FCN-Transformer. We expect our proposed method can benefit and accelerate the development of accurate retinal vessel segmentation in this field.
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Affiliation(s)
- Lingling Du
- Department of Ophthalmology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Hanruo Liu
- The Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Lan Zhang
- Department of Cardiovascular, Fourth Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Yao Lu
- Department of Ophthalmology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Mengyao Li
- Department of Ophthalmology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yang Hu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Yi Zhang
- Department of Ophthalmology, The First Affiliated Hospital of Harbin Medical University, Harbin, China.
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17
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Hu W, Zhang W, Zhou Y, Luo Y, Sun X, Xu H, Shi S, Li T, Xu Y, Yang Q, Qiu Y, Zhu F, Dai H. MecDDI: Clarified Drug-Drug Interaction Mechanism Facilitating Rational Drug Use and Potential Drug-Drug Interaction Prediction. J Chem Inf Model 2023; 63:1626-1636. [PMID: 36802582 DOI: 10.1021/acs.jcim.2c01656] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
Abstract
Drug-drug interactions (DDIs) are a major concern in clinical practice and have been recognized as one of the key threats to public health. To address such a critical threat, many studies have been conducted to clarify the mechanism underlying each DDI, based on which alternative therapeutic strategies are successfully proposed. Moreover, artificial intelligence-based models for predicting DDIs, especially multilabel classification models, are highly dependent on a reliable DDI data set with clear mechanistic information. These successes highlight the imminent necessity to have a platform providing mechanistic clarifications for a large number of existing DDIs. However, no such platform is available yet. In this study, a platform entitled "MecDDI" was therefore introduced to systematically clarify the mechanisms underlying the existing DDIs. This platform is unique in (a) clarifying the mechanisms underlying over 1,78,000 DDIs by explicit descriptions and graphic illustrations and (b) providing a systematic classification for all collected DDIs based on the clarified mechanisms. Due to the long-lasting threats of DDIs to public health, MecDDI could offer medical scientists a clear clarification of DDI mechanisms, support healthcare professionals to identify alternative therapeutics, and prepare data for algorithm scientists to predict new DDIs. MecDDI is now expected as an indispensable complement to the available pharmaceutical platforms and is freely accessible at: https://idrblab.org/mecddi/.
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Affiliation(s)
- Wei Hu
- Department of Pharmacy, Center of Clinical Pharmacology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Wei Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Ying Zhou
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou 310000, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Xiuna Sun
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Huimin Xu
- Department of Pharmacy, Center of Clinical Pharmacology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Shuiyang Shi
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Teng Li
- Department of Pharmacy, Center of Clinical Pharmacology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Yichao Xu
- Department of Pharmacy, Center of Clinical Pharmacology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Qianqian Yang
- Department of Pharmacy, Affiliated Hangzhou First Peoples Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China.,Clinical Pharmacy Research Center, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Yunqing Qiu
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou 310000, China
| | - Feng Zhu
- Department of Pharmacy, Center of Clinical Pharmacology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China.,College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Haibin Dai
- Department of Pharmacy, Center of Clinical Pharmacology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China.,Clinical Pharmacy Research Center, Zhejiang University School of Medicine, Hangzhou 310009, China
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