1
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Tian Z, Yu Y, Ni F, Zou Q. Drug-target interaction prediction with collaborative contrastive learning and adaptive self-paced sampling strategy. BMC Biol 2024; 22:216. [PMID: 39334132 PMCID: PMC11437672 DOI: 10.1186/s12915-024-02012-x] [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: 06/18/2024] [Accepted: 09/06/2024] [Indexed: 09/30/2024] Open
Abstract
BACKGROUND Drug-target interaction (DTI) prediction plays a pivotal role in drug discovery and drug repositioning, enabling the identification of potential drug candidates. However, most previous approaches often do not fully utilize the complementary relationships among multiple biological networks, which limits their ability to learn more consistent representations. Additionally, the selection strategy of negative samples significantly affects the performance of contrastive learning methods. RESULTS In this study, we propose CCL-ASPS, a novel deep learning model that incorporates Collaborative Contrastive Learning (CCL) and Adaptive Self-Paced Sampling strategy (ASPS) for drug-target interaction prediction. CCL-ASPS leverages multiple networks to learn the fused embeddings of drugs and targets, ensuring their consistent representations from individual networks. Furthermore, ASPS dynamically selects more informative negative sample pairs for contrastive learning. Experiment results on the established dataset demonstrate that CCL-ASPS achieves significant improvements compared to current state-of-the-art methods. Moreover, ablation experiments confirm the contributions of the proposed CCL and ASPS strategies. CONCLUSIONS By integrating Collaborative Contrastive Learning and Adaptive Self-Paced Sampling, the proposed CCL-ASPS effectively addresses the limitations of previous methods. This study demonstrates that CCL-ASPS achieves notable improvements in DTI predictive performance compared to current state-of-the-art approaches. The case study and cold start experiments further illustrate the capability of CCL-ASPS to effectively predict previously unknown DTI, potentially facilitating the identification of new drug-target interactions.
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Affiliation(s)
- Zhen Tian
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, 450001, Henan, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Yue Yu
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, 450001, Henan, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Fengming Ni
- Department of Gastroenterology, The First Hospital of Jilin University, Changchun, 130021, China.
| | - Quan Zou
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
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2
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Fang J, Zhang DF, Xie K, Xu L, Bi XA. Bilinear Perceptual Fusion Algorithm Based on Brain Functional and Structural Data for ASD Diagnosis and Regions of Interest Identification. Interdiscip Sci 2024:10.1007/s12539-024-00651-w. [PMID: 39254805 DOI: 10.1007/s12539-024-00651-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 08/10/2024] [Accepted: 08/12/2024] [Indexed: 09/11/2024]
Abstract
Autism spectrum disorder (ASD) is a serious mental disorder with a complex pathogenesis mechanism and variable presentation among individuals. Although many deep learning algorithms have been used to diagnose ASD, most of them focus on a single modality of data, resulting in limited information extraction and poor stability. In this paper, we propose a bilinear perceptual fusion (BPF) algorithm that leverages data from multiple modalities. In our algorithm, different schemes are used to extract features according to the characteristics of functional and structural data. Through bilinear operations, the associations between the functional and structural features of each region of interest (ROI) are captured. Then the associations are used to integrate the feature representation. Graph convolutional neural networks (GCNs) can effectively utilize topology and node features in brain network analysis. Therefore, we design a deep learning framework called BPF-GCN and conduct experiments on publicly available ASD dataset. The results show that the classification accuracy of BPF-GCN reached 82.35%, surpassing existing methods. This demonstrates the superiority of its classification performance, and the framework can extract ROIs related to ASD. Our work provides a valuable reference for the timely diagnosis and treatment of ASD.
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Affiliation(s)
- Jinxiong Fang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China
| | - Da-Fang Zhang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China.
| | - Kun Xie
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China
| | - Luyun Xu
- College of Business, Hunan Normal University, Changsha, 410081, China
| | - Xia-An Bi
- College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China.
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3
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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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4
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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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5
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Bai H, Lu S, Zhang T, Cui H, Nakaguchi T, Xuan P. Graph reasoning method enhanced by relational transformers and knowledge distillation for drug-related side effect prediction. iScience 2024; 27:109571. [PMID: 38799562 PMCID: PMC11126883 DOI: 10.1016/j.isci.2024.109571] [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] [Received: 08/06/2023] [Revised: 09/29/2023] [Accepted: 03/22/2024] [Indexed: 05/29/2024] Open
Abstract
Identifying the side effects related to drugs is beneficial for reducing the risk of drug development failure and saving the drug development cost. We proposed a graph reasoning method, RKDSP, to fuse the semantics of multiple connection relationships, the local knowledge within each meta-path, the global knowledge among multiple meta-paths, and the attributes of the drug and side effect node pairs. We constructed drug-side effect heterogeneous graphs consisting of the drugs, side effects, and their similarity and association connections. Multiple relational transformers were established to learn node features from diverse meta-path semantic perspectives. A knowledge distillation module was constructed to learn local and global knowledge of multiple meta-paths. Finally, an adaptive convolutional neural network-based strategy was presented to adaptively encode the attributes of each drug-side effect node pair. The experimental results demonstrated that RKDSP outperforms the compared state-of-the-art prediction approaches.
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Affiliation(s)
- Honglei Bai
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
| | - Siyuan Lu
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
| | - Tiangang Zhang
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
- School of Mathematical Science, Heilongjiang University, Harbin, China
| | - Hui Cui
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC, Australia
| | - Toshiya Nakaguchi
- Center for Frontier Medical Engineering, Chiba University, Chiba, Japan
| | - Ping Xuan
- Department of Computer Science and Technology, Shantou University, Shantou, China
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6
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Sun C, Xu C, Li H, Bo H, Ma L, Li H. A novel multi-feature fusion attention neural network for the recognition of epileptic EEG signals. Front Comput Neurosci 2024; 18:1393122. [PMID: 38962654 PMCID: PMC11219577 DOI: 10.3389/fncom.2024.1393122] [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: 02/28/2024] [Accepted: 05/20/2024] [Indexed: 07/05/2024] Open
Abstract
Epilepsy is a common chronic brain disorder. Detecting epilepsy by observing electroencephalography (EEG) is the main method neurologists use, but this method is time-consuming. EEG signals are non-stationary, nonlinear, and often highly noisy, so it remains challenging to recognize epileptic EEG signals more accurately and automatically. This paper proposes a novel classification system of epileptic EEG signals for single-channel EEG based on the attention network that integrates time-frequency and nonlinear dynamic features. The proposed system has three novel modules. The first module constructs the Hilbert spectrum (HS) with high time-frequency resolution into a two-channel parallel convolutional network. The time-frequency features are fully extracted by complementing the high-dimensional features of the two branches. The second module constructs a grayscale recurrence plot (GRP) that contains more nonlinear dynamic features than traditional RP, fed into the residual-connected convolution module for effective learning of nonlinear dynamic features. The third module is the feature fusion module based on a self-attention mechanism to assign optimal weights to different types of features and further enhance the information extraction capability of the system. Therefore, the system is named HG-SANet. The results of several classification tasks on the Bonn EEG database and the Bern-Barcelona EEG database show that the HG-SANet can effectively capture the contribution degree of the extracted features from different domains, significantly enhance the expression ability of the model, and improve the accuracy of the recognition of epileptic EEG signals. The HG-SANet can improve the diagnosis and treatment efficiency of epilepsy and has broad application prospects in the fields of brain disease diagnosis.
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Affiliation(s)
- Congshan Sun
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Cong Xu
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Hongwei Li
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Hongjian Bo
- Shenzhen Academy of Aerospace Technology, Shenzhen, China
| | - Lin Ma
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Haifeng Li
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
- Shenzhen Academy of Aerospace Technology, Shenzhen, China
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7
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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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8
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He SH, Yun L, Yi HC. Accurate prediction of drug combination risk levels based on relational graph convolutional network and multi-head attention. J Transl Med 2024; 22:572. [PMID: 38880914 PMCID: PMC11180398 DOI: 10.1186/s12967-024-05372-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 06/02/2024] [Indexed: 06/18/2024] Open
Abstract
BACKGROUND Accurately identifying the risk level of drug combinations is of great significance in investigating the mechanisms of combination medication and adverse reactions. Most existing methods can only predict whether there is an interaction between two drugs, but cannot directly determine their accurate risk level. METHODS In this study, we propose a multi-class drug combination risk prediction model named AERGCN-DDI, utilizing a relational graph convolutional network with a multi-head attention mechanism. Drug-drug interaction events with varying risk levels are modeled as a heterogeneous information graph. Attribute features of drug nodes and links are learned based on compound chemical structure information. Finally, the AERGCN-DDI model is proposed to predict drug combination risk level based on heterogenous graph neural network and multi-head attention modules. RESULTS To evaluate the effectiveness of the proposed method, five-fold cross-validation and ablation study were conducted. Furthermore, we compared its predictive performance with baseline models and other state-of-the-art methods on two benchmark datasets. Empirical studies demonstrated the superior performances of AERGCN-DDI. CONCLUSIONS AERGCN-DDI emerges as a valuable tool for predicting the risk levels of drug combinations, thereby aiding in clinical medication decision-making, mitigating severe drug side effects, and enhancing patient clinical prognosis.
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Affiliation(s)
- Shi-Hui He
- School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China
- Engineering Research Center of Computer Vision and Intelligent Control Technology, Department of Education, Kunming, 650500, China
| | - Lijun Yun
- School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China.
- Engineering Research Center of Computer Vision and Intelligent Control Technology, Department of Education, Kunming, 650500, China.
| | - Hai-Cheng Yi
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710129, China.
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9
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Hu B, Yu Z, Li M. MPHGCL-DDI: Meta-Path-Based Heterogeneous Graph Contrastive Learning for Drug-Drug Interaction Prediction. Molecules 2024; 29:2483. [PMID: 38893359 PMCID: PMC11173658 DOI: 10.3390/molecules29112483] [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: 04/21/2024] [Revised: 05/17/2024] [Accepted: 05/22/2024] [Indexed: 06/21/2024] Open
Abstract
The combinatorial therapy with multiple drugs may lead to unexpected drug-drug interactions (DDIs) and result in adverse reactions to patients. Predicting DDI events can mitigate the potential risks of combinatorial therapy and enhance drug safety. In recent years, deep models based on heterogeneous graph representation learning have attracted widespread interest in DDI event prediction and have yielded satisfactory results, but there is still room for improvement in prediction performance. In this study, we proposed a meta-path-based heterogeneous graph contrastive learning model, MPHGCL-DDI, for DDI event prediction. The model constructs two contrastive views based on meta-paths: an average graph view and an augmented graph view. The former represents that there are connections between drugs, while the latter reveals how the drugs connect with each other. We defined three levels of data augmentation schemes in the augmented graph view and adopted a combination of three losses in the model training phase: multi-relation prediction loss, unsupervised contrastive loss and supervised contrastive loss. Furthermore, the model incorporates indirect drug information, protein-protein interactions (PPIs), to reveal latent relations of drugs. We evaluated MPHGCL-DDI on three different tasks of two datasets. Experimental results demonstrate that MPHGCL-DDI surpasses several state-of-the-art methods in performance.
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Affiliation(s)
- Baofang Hu
- School of Data and Computer Science, Shandong Women’s University, Jinan 250030, China;
| | - Zhenmei Yu
- School of Data and Computer Science, Shandong Women’s University, Jinan 250030, China;
| | - Mingke Li
- School of Information Science and Engineering, University of Jinan, Jinan 250024, China;
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10
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Lin J, Hong B, Cai Z, Lu P, Lin K. MASMDDI: multi-layer adaptive soft-mask graph neural network for drug-drug interaction prediction. Front Pharmacol 2024; 15:1369403. [PMID: 38831885 PMCID: PMC11144894 DOI: 10.3389/fphar.2024.1369403] [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: 01/12/2024] [Accepted: 04/23/2024] [Indexed: 06/05/2024] Open
Abstract
Accurately predicting Drug-Drug Interaction (DDI) is a critical and challenging aspect of the drug discovery process, particularly in preventing adverse reactions in patients undergoing combination therapy. However, current DDI prediction methods often overlook the interaction information between chemical substructures of drugs, focusing solely on the interaction information between drugs and failing to capture sufficient chemical substructure details. To address this limitation, we introduce a novel DDI prediction method: Multi-layer Adaptive Soft Mask Graph Neural Network (MASMDDI). Specifically, we first design a multi-layer adaptive soft mask graph neural network to extract substructures from molecular graphs. Second, we employ an attention mechanism to mine substructure feature information and update latent features. In this process, to optimize the final feature representation, we decompose drug-drug interactions into pairwise interaction correlations between the core substructures of each drug. Third, we use these features to predict the interaction probabilities of DDI tuples and evaluate the model using real-world datasets. Experimental results demonstrate that the proposed model outperforms state-of-the-art methods in DDI prediction. Furthermore, MASMDDI exhibits excellent performance in predicting DDIs of unknown drugs in two tasks that are more aligned with real-world scenarios. In particular, in the transductive scenario using the DrugBank dataset, the ACC and AUROC and AUPRC scores of MASMDDI are 0.9596, 0.9903, and 0.9894, which are 2% higher than the best performing baseline.
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Affiliation(s)
- Junpeng Lin
- School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China
| | - Binsheng Hong
- School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China
| | - Zhongqi Cai
- 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
| | - Kaibiao Lin
- School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China
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11
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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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12
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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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13
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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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14
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Jinsong S, Qifeng J, Xing C, Hao Y, Wang L. Molecular fragmentation as a crucial step in the AI-based drug development pathway. Commun Chem 2024; 7:20. [PMID: 38302655 PMCID: PMC10834946 DOI: 10.1038/s42004-024-01109-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 01/19/2024] [Indexed: 02/03/2024] Open
Abstract
The AI-based small molecule drug discovery has become a significant trend at the intersection of computer science and life sciences. In the pursuit of novel compounds, fragment-based drug discovery has emerged as a novel approach. The Generative Pre-trained Transformers (GPT) model has showcased remarkable prowess across various domains, rooted in its pre-training and representation learning of fundamental linguistic units. Analogous to natural language, molecular encoding, as a form of chemical language, necessitates fragmentation aligned with specific chemical logic for accurate molecular encoding. This review provides a comprehensive overview of the current state of the art in molecular fragmentation. We systematically summarize the approaches and applications of various molecular fragmentation techniques, with special emphasis on the characteristics and scope of applicability of each technique, and discuss their applications. We also provide an outlook on the current development trends of molecular fragmentation techniques, including some potential research directions and challenges.
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Affiliation(s)
- Shao Jinsong
- Nantong University, School of Information Science and Technology, Nantong, China
| | - Jia Qifeng
- Nantong University, School of Information Science and Technology, Nantong, China
| | - Chen Xing
- Nantong University, School of Information Science and Technology, Nantong, China
| | - Yajie Hao
- Nantong University, School of Information Science and Technology, Nantong, China
| | - Li Wang
- Nantong University, Research Center for Intelligence Information Technology, Nantong, China.
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15
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Chen S, Semenov I, Zhang F, Yang Y, Geng J, Feng X, Meng Q, Lei K. An effective framework for predicting drug-drug interactions based on molecular substructures and knowledge graph neural network. Comput Biol Med 2024; 169:107900. [PMID: 38199213 DOI: 10.1016/j.compbiomed.2023.107900] [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: 09/11/2023] [Revised: 11/27/2023] [Accepted: 12/23/2023] [Indexed: 01/12/2024]
Abstract
Drug-drug interactions (DDIs) play a central role in drug research, as the simultaneous administration of multiple drugs can have harmful or beneficial effects. Harmful interactions lead to adverse reactions, some of which can be life-threatening, while beneficial interactions can promote efficacy. Therefore, it is crucial for physicians, patients, and the research community to identify potential DDIs. Although many AI-based techniques have been proposed for predicting DDIs, most existing computational models primarily focus on integrating multiple data sources or combining popular embedding methods. Researchers often overlook the valuable information within the molecular structure of drugs or only consider the structural information of drugs, neglecting the relationship or topological information between drugs and other biological objects. In this study, we propose MSKG-DDI - a two-component framework that incorporates the Drug Chemical Structure Graph-based component and the Drug Knowledge Graph-based component to capture multimodal characteristics of drugs. Subsequently, a multimodal fusion neural layer is utilized to explore the complementarity between multimodal representations of drugs. Extensive experiments were conducted using two real-world datasets, and the results demonstrate that MSKG-DDI outperforms other state-of-the-art models in binary-class, multi-class, and multi-label prediction tasks under both transductive and inductive settings. Furthermore, the ablation analysis further confirms the practical usefulness of MSKG-DDI.
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Affiliation(s)
- Siqi Chen
- School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing, 400074, China
| | - Ivan Semenov
- College of Intelligence and Computing, Tianjin University, Tianjin, 300072, China
| | - Fengyun Zhang
- College of Intelligence and Computing, Tianjin University, Tianjin, 300072, China
| | - Yang Yang
- College of Intelligence and Computing, Tianjin University, Tianjin, 300072, China
| | - Jie Geng
- TianJin Chest Hospital, Tianjin University, Tianjin, 300222, China
| | - Xuequan Feng
- Tianjin First Central Hospital, Tianjin, 300192, China.
| | - Qinghua Meng
- Tianjin Key Laboratory of Sports Physiology and Sports Medicine, Tianjin University of Sport, Tianjin, 301617, China
| | - Kaiyou Lei
- College of Computer and Information Science, Southwest University, Chongqing, 400715, China
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16
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Cheng N, Bi C, Shi Y, Liu M, Cao A, Ren M, Xia J, Liang Z. Effect Predictor of Driver Synonymous Mutations Based on Multi-Feature Fusion and Iterative Feature Representation Learning. IEEE J Biomed Health Inform 2024; 28:1144-1151. [PMID: 38096097 DOI: 10.1109/jbhi.2023.3343075] [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: 12/19/2023]
Abstract
Accurate identification of driver mutations is crucial in genetic studies of human cancers. While numerous cancer driver missense mutations have been identified, research into potential cancer drivers for synonymous mutations has shown limited success to date. Here, we developed a novel machine learning framework, epSMic, for predicting cancer driver synonymous mutations. epSMic employs an iterative feature representation scheme that facilitates the learning of discriminative features from various sequential models in a supervised iterative mode. We constructed the benchmark datasets and encoded the embedding sequence, physicochemical property, and basic information such as conservation and splicing feature. The evaluation results on benchmark test datasets demonstrate that epSMic outperforms existing methods, making it a valuable tool for researchers in identifying functional synonymous mutations in cancer. We hope epSMic can enable researchers to concentrate on synonymous mutations that have a functional impact on cancer.
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17
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Zhou B, Ran B, Chen L. A GraphSAGE-based model with fingerprints only to predict drug-drug interactions. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:2922-2942. [PMID: 38454713 DOI: 10.3934/mbe.2024130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/09/2024]
Abstract
Drugs are an effective way to treat various diseases. Some diseases are so complicated that the effect of a single drug for such diseases is limited, which has led to the emergence of combination drug therapy. The use multiple drugs to treat these diseases can improve the drug efficacy, but it can also bring adverse effects. Thus, it is essential to determine drug-drug interactions (DDIs). Recently, deep learning algorithms have become popular to design DDI prediction models. However, most deep learning-based models need several types of drug properties, inducing the application problems for drugs without these properties. In this study, a new deep learning-based model was designed to predict DDIs. For wide applications, drugs were first represented by commonly used properties, referred to as fingerprint features. Then, these features were perfectly fused with the drug interaction network by a type of graph convolutional network method, GraphSAGE, yielding high-level drug features. The inner product was adopted to score the strength of drug pairs. The model was evaluated by 10-fold cross-validation, resulting in an AUROC of 0.9704 and AUPR of 0.9727. Such performance was better than the previous model which directly used drug fingerprint features and was competitive compared with some other previous models that used more drug properties. Furthermore, the ablation tests indicated the importance of the main parts of the model, and we analyzed the strengths and limitations of a model for drugs with different degrees in the network. This model identified some novel DDIs that may bring expected benefits, such as the combination of PEA and cannabinol that may produce better effects. DDIs that may cause unexpected side effects have also been discovered, such as the combined use of WIN 55,212-2 and cannabinol. These DDIs can provide novel insights for treating complex diseases or avoiding adverse drug events.
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Affiliation(s)
- Bo Zhou
- Institute of Wound Prevention and Treatment, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Basic Medical Sciences, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
| | - Bing Ran
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
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18
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Wang J, Liao N, Du X, Chen Q, Wei B. A semi-supervised approach for the integration of multi-omics data based on transformer multi-head self-attention mechanism and graph convolutional networks. BMC Genomics 2024; 25:86. [PMID: 38254021 PMCID: PMC10802018 DOI: 10.1186/s12864-024-09985-7] [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: 11/03/2023] [Accepted: 01/07/2024] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Comprehensive analysis of multi-omics data is crucial for accurately formulating effective treatment plans for complex diseases. Supervised ensemble methods have gained popularity in recent years for multi-omics data analysis. However, existing research based on supervised learning algorithms often fails to fully harness the information from unlabeled nodes and overlooks the latent features within and among different omics, as well as the various associations among features. Here, we present a novel multi-omics integrative method MOSEGCN, based on the Transformer multi-head self-attention mechanism and Graph Convolutional Networks(GCN), with the aim of enhancing the accuracy of complex disease classification. MOSEGCN first employs the Transformer multi-head self-attention mechanism and Similarity Network Fusion (SNF) to separately learn the inherent correlations of latent features within and among different omics, constructing a comprehensive view of diseases. Subsequently, it feeds the learned crucial information into a self-ensembling Graph Convolutional Network (SEGCN) built upon semi-supervised learning methods for training and testing, facilitating a better analysis and utilization of information from multi-omics data to achieve precise classification of disease subtypes. RESULTS The experimental results show that MOSEGCN outperforms several state-of-the-art multi-omics integrative analysis approaches on three types of omics data: mRNA expression data, microRNA expression data, and DNA methylation data, with accuracy rates of 83.0% for Alzheimer's disease and 86.7% for breast cancer subtyping. Furthermore, MOSEGCN exhibits strong generalizability on the GBM dataset, enabling the identification of important biomarkers for related diseases. CONCLUSION MOSEGCN explores the significant relationship information among different omics and within each omics' latent features, effectively leveraging labeled and unlabeled information to further enhance the accuracy of complex disease classification. It also provides a promising approach for identifying reliable biomarkers, paving the way for personalized medicine.
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Affiliation(s)
- Jiahui Wang
- School of Computer and Information Security, Guilin University of Electronic Technology, No. 1 Jinji Road, Guilin City, 541004, Guangxi Zhuang Autonomous Region, China
| | - Nanqing Liao
- School of Medical, Guangxi University, No. 100 East University Road, Nanning, 530004, Guangxi, China
| | - Xiaofei Du
- School of Computer and Information Security, Guilin University of Electronic Technology, No. 1 Jinji Road, Guilin City, 541004, Guangxi Zhuang Autonomous Region, China
| | - Qingfeng Chen
- School of Computer, Electronics and Information, Guangxi University, No. 100 East University Road, Nanning, 530004, Guangxi, China.
| | - Bizhong Wei
- School of Computer and Information Security, Guilin University of Electronic Technology, No. 1 Jinji Road, Guilin City, 541004, Guangxi Zhuang Autonomous Region, China.
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19
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Zhang C, Zang T, Zhao T. KGE-UNIT: toward the unification of molecular interactions prediction based on knowledge graph and multi-task learning on drug discovery. Brief Bioinform 2024; 25:bbae043. [PMID: 38348746 PMCID: PMC10939374 DOI: 10.1093/bib/bbae043] [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: 07/04/2023] [Revised: 12/29/2023] [Accepted: 01/23/2024] [Indexed: 02/15/2024] Open
Abstract
The prediction of molecular interactions is vital for drug discovery. Existing methods often focus on individual prediction tasks and overlook the relationships between them. Additionally, certain tasks encounter limitations due to insufficient data availability, resulting in limited performance. To overcome these limitations, we propose KGE-UNIT, a unified framework that combines knowledge graph embedding (KGE) and multi-task learning, for simultaneous prediction of drug-target interactions (DTIs) and drug-drug interactions (DDIs) and enhancing the performance of each task, even when data availability is limited. Via KGE, we extract heterogeneous features from the drug knowledge graph to enhance the structural features of drug and protein nodes, thereby improving the quality of features. Additionally, employing multi-task learning, we introduce an innovative predictor that comprises the task-aware Convolutional Neural Network-based (CNN-based) encoder and the task-aware attention decoder which can fuse better multimodal features, capture the contextual interactions of molecular tasks and enhance task awareness, leading to improved performance. Experiments on two imbalanced datasets for DTIs and DDIs demonstrate the superiority of KGE-UNIT, achieving high area under the receiver operating characteristics curves (AUROCs) (0.942, 0.987) and area under the precision-recall curve ( AUPRs) (0.930, 0.980) for DTIs and high AUROCs (0.975, 0.989) and AUPRs (0.966, 0.988) for DDIs. Notably, on the LUO dataset where the data were more limited, KGE-UNIT exhibited a more pronounced improvement, with increases of 4.32$\%$ in AUROC and 3.56$\%$ in AUPR for DTIs and 6.56$\%$ in AUROC and 8.17$\%$ in AUPR for DDIs. The scalability of KGE-UNIT is demonstrated through its extension to protein-protein interactions prediction, ablation studies and case studies further validate its effectiveness.
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Affiliation(s)
- Chengcheng Zhang
- Department of Computer Science, Harbin Institute of Technology, Harbin, 150001, China
| | - Tianyi Zang
- Department of Computer Science, Harbin Institute of Technology, Harbin, 150001, China
| | - Tianyi Zhao
- School of Medicine and Health, Harbin Institute of Technology, Harbin, 150001, China
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20
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Huang M, Jiang Z, Guo S. Phar-LSTM: a pharmacological representation-based LSTM network for drug-drug interaction extraction. PeerJ 2023; 11:e16606. [PMID: 38107590 PMCID: PMC10725669 DOI: 10.7717/peerj.16606] [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: 02/07/2023] [Accepted: 11/15/2023] [Indexed: 12/19/2023] Open
Abstract
Pharmacological drug interactions are among the most common causes of medication errors. Many different methods have been proposed to extract drug-drug interactions from the literature to reduce medication errors over the last few years. However, the performance of these methods can be further improved. In this paper, we present a Pharmacological representation-based Long Short-Term Memory (LSTM) network named Phar-LSTM. In this method, a novel embedding strategy is proposed to extract pharmacological representations from the biomedical literature, and the information related to the target drug is considered. Then, an LSTM-based multi-task learning scheme is introduced to extract features from the different but related tasks according to their corresponding pharmacological representations. Finally, the extracted features are fed to the SoftMax classifier of the corresponding task. Experimental results on the DDIExtraction 2011 and DDIExtraction 2013 corpuses show that the performance of Phar-LSTM is competitive compared with other state-of-the-art methods. Our Python implementation and the corresponding data of Phar-LSTM are available by using the DOI 10.5281/zenodo.8249384.
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Affiliation(s)
- Mingqing Huang
- School of Software Engineering, Shenzhen Institute of Information Technology, Shenzhen, Guangdong, China
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Zhenchao Jiang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Shun Guo
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
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21
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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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22
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Zhao Y, Yin J, Zhang L, Zhang Y, Chen X. Drug-drug interaction prediction: databases, web servers and computational models. Brief Bioinform 2023; 25:bbad445. [PMID: 38113076 PMCID: PMC10782925 DOI: 10.1093/bib/bbad445] [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: 07/21/2023] [Revised: 10/26/2023] [Accepted: 11/14/2023] [Indexed: 12/21/2023] Open
Abstract
In clinical treatment, two or more drugs (i.e. drug combination) are simultaneously or successively used for therapy with the purpose of primarily enhancing the therapeutic efficacy or reducing drug side effects. However, inappropriate drug combination may not only fail to improve efficacy, but even lead to adverse reactions. Therefore, according to the basic principle of improving the efficacy and/or reducing adverse reactions, we should study drug-drug interactions (DDIs) comprehensively and thoroughly so as to reasonably use drug combination. In this review, we first introduced the basic conception and classification of DDIs. Further, some important publicly available databases and web servers about experimentally verified or predicted DDIs were briefly described. As an effective auxiliary tool, computational models for predicting DDIs can not only save the cost of biological experiments, but also provide relevant guidance for combination therapy to some extent. Therefore, we summarized three types of prediction models (including traditional machine learning-based models, deep learning-based models and score function-based models) proposed during recent years and discussed the advantages as well as limitations of them. Besides, we pointed out the problems that need to be solved in the future research of DDIs prediction and provided corresponding suggestions.
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Affiliation(s)
- Yan Zhao
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Jun Yin
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Li Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Yong Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Xing Chen
- School of Science, Jiangnan University, Wuxi 214122, China
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23
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Zhang Y, Liu C, Liu M, Liu T, Lin H, Huang CB, Ning L. Attention is all you need: utilizing attention in AI-enabled drug discovery. Brief Bioinform 2023; 25:bbad467. [PMID: 38189543 PMCID: PMC10772984 DOI: 10.1093/bib/bbad467] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 11/03/2023] [Accepted: 11/25/2023] [Indexed: 01/09/2024] Open
Abstract
Recently, attention mechanism and derived models have gained significant traction in drug development due to their outstanding performance and interpretability in handling complex data structures. This review offers an in-depth exploration of the principles underlying attention-based models and their advantages in drug discovery. We further elaborate on their applications in various aspects of drug development, from molecular screening and target binding to property prediction and molecule generation. Finally, we discuss the current challenges faced in the application of attention mechanisms and Artificial Intelligence technologies, including data quality, model interpretability and computational resource constraints, along with future directions for research. Given the accelerating pace of technological advancement, we believe that attention-based models will have an increasingly prominent role in future drug discovery. We anticipate that these models will usher in revolutionary breakthroughs in the pharmaceutical domain, significantly accelerating the pace of drug development.
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Affiliation(s)
- Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Caiqi Liu
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, No.150 Haping Road, Nangang District, Harbin, Heilongjiang 150081, China
- Key Laboratory of Molecular Oncology of Heilongjiang Province, No.150 Haping Road, Nangang District, Harbin, Heilongjiang 150081, China
| | - Mujiexin Liu
- Chongqing Key Laboratory of Sichuan-Chongqing Co-construction for Diagnosis and Treatment of Infectious Diseases Integrated Traditional Chinese and Western Medicine, College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Tianyuan Liu
- Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Japan
| | - Hao Lin
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Cheng-Bing Huang
- School of Computer Science and Technology, Aba Teachers University, Aba, China
| | - Lin Ning
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu 611844, China
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24
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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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25
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Zhou Q, Zhang Y, Wang S, Wu D. Drug-drug interaction prediction based on local substructure features and their complements. J Mol Graph Model 2023; 124:108557. [PMID: 37390789 DOI: 10.1016/j.jmgm.2023.108557] [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: 03/24/2023] [Revised: 04/27/2023] [Accepted: 06/17/2023] [Indexed: 07/02/2023]
Abstract
The properties of drugs may undergo changes when multiple drugs are co-administered to treat co-existing or complex diseases, potentially leading to unforeseen drug-drug interactions (DDIs). Therefore, predicting potential drug-drug interactions has been an important task in pharmaceutical research. However, the following challenges remain: (1) existing methods do not work very well in cold-start scenarios, and (2) the interpretability of existing methods is not satisfactory. To address these challenges, we proposed a multi-channel feature fusion method based on local substructure features of drugs and their complements (LSFC). The local substructure features are extracted from each drug, interacted with those of another drug, and then integrated with the global features of two drugs for DDI prediction. We evaluated LSFC on two real-world DDI datasets in worm-start and cold-start scenarios. Comprehensive experiments demonstrate that LSFC consistently improved DDI prediction performance compared with the start-of-the-art methods. Moreover, visual inspection results showed that LSFC can detect crucial substructures of drugs for DDIs, providing interpretable DDI prediction. The source codes and data are available at https://github.com/Zhang-Yang-ops/LSFC.
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Affiliation(s)
- Qing Zhou
- College of Computer Science, Chongqing University, Chongqing 400044, China.
| | - Yang Zhang
- College of Computer Science, Chongqing University, Chongqing 400044, China.
| | - Siyuan Wang
- College of Computer Science, Chongqing University, Chongqing 400044, China.
| | - Dayu Wu
- College of Computer Science, Chongqing University, Chongqing 400044, China.
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26
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Meng R, Yin S, Sun J, Hu H, Zhao Q. scAAGA: Single cell data analysis framework using asymmetric autoencoder with gene attention. Comput Biol Med 2023; 165:107414. [PMID: 37660567 DOI: 10.1016/j.compbiomed.2023.107414] [Citation(s) in RCA: 54] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 08/02/2023] [Accepted: 08/28/2023] [Indexed: 09/05/2023]
Abstract
In recent years, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique for investigating cellular heterogeneity and structure. However, analyzing scRNA-seq data remains challenging, especially in the context of COVID-19 research. Single-cell clustering is a key step in analyzing scRNA-seq data, and deep learning methods have shown great potential in this area. In this work, we propose a novel scRNA-seq analysis framework called scAAGA. Specifically, we utilize an asymmetric autoencoder with a gene attention module to learn important gene features adaptively from scRNA-seq data, with the aim of improving the clustering effect. We apply scAAGA to COVID-19 peripheral blood mononuclear cell (PBMC) scRNA-seq data and compare its performance with state-of-the-art methods. Our results consistently demonstrate that scAAGA outperforms existing methods in terms of adjusted rand index (ARI), normalized mutual information (NMI), and adjusted mutual information (AMI) scores, achieving improvements ranging from 2.8% to 27.8% in NMI scores. Additionally, we discuss a data augmentation technology to expand the datasets and improve the accuracy of scAAGA. Overall, scAAGA presents a robust tool for scRNA-seq data analysis, enhancing the accuracy and reliability of clustering results in COVID-19 research.
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Affiliation(s)
- Rui Meng
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China
| | - Shuaidong Yin
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China
| | - Jianqiang Sun
- School of Information Science and Engineering, Linyi University, Linyi, 276000, China
| | - Huan Hu
- Institute of Applied Genomics, Fuzhou University, Fuzhou, 350108, China.
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China.
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27
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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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28
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Zhu Y, Zhao L, Wen N, Wang J, Wang C. DataDTA: a multi-feature and dual-interaction aggregation framework for drug-target binding affinity prediction. Bioinformatics 2023; 39:btad560. [PMID: 37688568 PMCID: PMC10516524 DOI: 10.1093/bioinformatics/btad560] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 05/09/2023] [Accepted: 09/07/2023] [Indexed: 09/11/2023] Open
Abstract
MOTIVATION Accurate prediction of drug-target binding affinity (DTA) is crucial for drug discovery. The increase in the publication of large-scale DTA datasets enables the development of various computational methods for DTA prediction. Numerous deep learning-based methods have been proposed to predict affinities, some of which only utilize original sequence information or complex structures, but the effective combination of various information and protein-binding pockets have not been fully mined. Therefore, a new method that integrates available key information is urgently needed to predict DTA and accelerate the drug discovery process. RESULTS In this study, we propose a novel deep learning-based predictor termed DataDTA to estimate the affinities of drug-target pairs. DataDTA utilizes descriptors of predicted pockets and sequences of proteins, as well as low-dimensional molecular features and SMILES strings of compounds as inputs. Specifically, the pockets were predicted from the three-dimensional structure of proteins and their descriptors were extracted as the partial input features for DTA prediction. The molecular representation of compounds based on algebraic graph features was collected to supplement the input information of targets. Furthermore, to ensure effective learning of multiscale interaction features, a dual-interaction aggregation neural network strategy was developed. DataDTA was compared with state-of-the-art methods on different datasets, and the results showed that DataDTA is a reliable prediction tool for affinities estimation. Specifically, the concordance index (CI) of DataDTA is 0.806 and the Pearson correlation coefficient (R) value is 0.814 on the test dataset, which is higher than other methods. AVAILABILITY AND IMPLEMENTATION The codes and datasets of DataDTA are available at https://github.com/YanZhu06/DataDTA.
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Affiliation(s)
- Yan Zhu
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
| | - Lingling Zhao
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
| | - Naifeng Wen
- School of Mechanical and Electrical Engineering, Dalian Minzu University, Dalian 116600, China
| | - Junjie Wang
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Chunyu Wang
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
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29
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Masumshah R, Eslahchi C. DPSP: a multimodal deep learning framework for polypharmacy side effects prediction. BIOINFORMATICS ADVANCES 2023; 3:vbad110. [PMID: 37701676 PMCID: PMC10493180 DOI: 10.1093/bioadv/vbad110] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 08/01/2023] [Accepted: 08/15/2023] [Indexed: 09/14/2023]
Abstract
Motivation Because unanticipated drug-drug interactions (DDIs) can result in severe bodily harm, identifying the adverse effects of polypharmacy is one of the most important tasks in human health. Over the past few decades, computational methods for predicting the adverse effects of polypharmacy have been developed. Results This article presents DPSP, a framework for predicting polypharmacy side effects based on the construction of novel drug features and the application of a deep neural network to predict DDIs. In the first step, a variety of drug information is evaluated, and a feature extraction method and the Jaccard similarity are used to determine similarities between two drugs. By combining these similarities, a novel feature vector is generated for each drug. In the second step, the method predicts DDIs for specific DDI events using a multimodal framework and drug feature vectors. On three benchmark datasets, the performance of DPSP is measured by comparing its results to those of several well-known methods, such as GNN-DDI, MSTE, MDF-SA-DDI, NNPS, DDIMDL, DNN, DeepDDI, KNN, LR, and RF. DPSP outperforms these classification methods based on a variety of classification metrics. The results indicate that the use of diverse drug information is effective and efficient for identifying DDI adverse effects. Availability and implementation The source code and datasets are available at https://github.com/raziyehmasumshah/DPSP.
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Affiliation(s)
- Raziyeh Masumshah
- Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran 1983969411, Iran
| | - Changiz Eslahchi
- Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran 1983969411, Iran
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran 193955746, Iran
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30
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Han CD, Wang CC, Huang L, Chen X. MCFF-MTDDI: multi-channel feature fusion for multi-typed drug-drug interaction prediction. Brief Bioinform 2023; 24:bbad215. [PMID: 37291761 DOI: 10.1093/bib/bbad215] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 05/11/2023] [Accepted: 05/21/2023] [Indexed: 06/10/2023] Open
Abstract
Adverse drug-drug interactions (DDIs) have become an increasingly serious problem in the medical and health system. Recently, the effective application of deep learning and biomedical knowledge graphs (KGs) have improved the DDI prediction performance of computational models. However, the problems of feature redundancy and KG noise also arise, bringing new challenges for researchers. To overcome these challenges, we proposed a Multi-Channel Feature Fusion model for multi-typed DDI prediction (MCFF-MTDDI). Specifically, we first extracted drug chemical structure features, drug pairs' extra label features, and KG features of drugs. Then, these different features were effectively fused by a multi-channel feature fusion module. Finally, multi-typed DDIs were predicted through the fully connected neural network. To our knowledge, we are the first to integrate the extra label information into KG-based multi-typed DDI prediction; besides, we innovatively proposed a novel KG feature learning method and a State Encoder to obtain target drug pairs' KG-based features which contained more abundant and more key drug-related KG information with less noise; furthermore, a Gated Recurrent Unit-based multi-channel feature fusion module was proposed in an innovative way to yield more comprehensive feature information about drug pairs, effectively alleviating the problem of feature redundancy. We experimented with four datasets in the multi-class and the multi-label prediction tasks to comprehensively evaluate the performance of MCFF-MTDDI for predicting interactions of known-known drugs, known-new drugs and new-new drugs. In addition, we further conducted ablation studies and case studies. All the results fully demonstrated the effectiveness of MCFF-MTDDI.
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Affiliation(s)
- Chen-Di Han
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China
| | - Chun-Chun Wang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China
- School of Science, Jiangnan University, Wuxi, 214122, China
| | - Li Huang
- The Future Laboratory, Tsinghua University, Beijing, 100084, China
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China
- School of Science, Jiangnan University, Wuxi, 214122, China
- Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, 221116, China
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31
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Jiang X, Hu Z, Wang S, Zhang Y. Deep Learning for Medical Image-Based Cancer Diagnosis. Cancers (Basel) 2023; 15:3608. [PMID: 37509272 PMCID: PMC10377683 DOI: 10.3390/cancers15143608] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 07/10/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023] Open
Abstract
(1) Background: The application of deep learning technology to realize cancer diagnosis based on medical images is one of the research hotspots in the field of artificial intelligence and computer vision. Due to the rapid development of deep learning methods, cancer diagnosis requires very high accuracy and timeliness as well as the inherent particularity and complexity of medical imaging. A comprehensive review of relevant studies is necessary to help readers better understand the current research status and ideas. (2) Methods: Five radiological images, including X-ray, ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), positron emission computed tomography (PET), and histopathological images, are reviewed in this paper. The basic architecture of deep learning and classical pretrained models are comprehensively reviewed. In particular, advanced neural networks emerging in recent years, including transfer learning, ensemble learning (EL), graph neural network, and vision transformer (ViT), are introduced. Five overfitting prevention methods are summarized: batch normalization, dropout, weight initialization, and data augmentation. The application of deep learning technology in medical image-based cancer analysis is sorted out. (3) Results: Deep learning has achieved great success in medical image-based cancer diagnosis, showing good results in image classification, image reconstruction, image detection, image segmentation, image registration, and image synthesis. However, the lack of high-quality labeled datasets limits the role of deep learning and faces challenges in rare cancer diagnosis, multi-modal image fusion, model explainability, and generalization. (4) Conclusions: There is a need for more public standard databases for cancer. The pre-training model based on deep neural networks has the potential to be improved, and special attention should be paid to the research of multimodal data fusion and supervised paradigm. Technologies such as ViT, ensemble learning, and few-shot learning will bring surprises to cancer diagnosis based on medical images.
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Grants
- RM32G0178B8 BBSRC
- MC_PC_17171 MRC, UK
- RP202G0230 Royal Society, UK
- AA/18/3/34220 BHF, UK
- RM60G0680 Hope Foundation for Cancer Research, UK
- P202PF11 GCRF, UK
- RP202G0289 Sino-UK Industrial Fund, UK
- P202ED10, P202RE969 LIAS, UK
- P202RE237 Data Science Enhancement Fund, UK
- 24NN201 Fight for Sight, UK
- OP202006 Sino-UK Education Fund, UK
- RM32G0178B8 BBSRC, UK
- 2023SJZD125 Major project of philosophy and social science research in colleges and universities in Jiangsu Province, China
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Affiliation(s)
- Xiaoyan Jiang
- School of Mathematics and Information Science, Nanjing Normal University of Special Education, Nanjing 210038, China; (X.J.); (Z.H.)
| | - Zuojin Hu
- School of Mathematics and Information Science, Nanjing Normal University of Special Education, Nanjing 210038, China; (X.J.); (Z.H.)
| | - Shuihua Wang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK;
| | - Yudong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK;
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32
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Lin X, Dai L, Zhou Y, Yu ZG, Zhang W, Shi JY, Cao DS, Zeng L, Chen H, Song B, Yu PS, Zeng X. Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction. Brief Bioinform 2023:bbad235. [PMID: 37401373 DOI: 10.1093/bib/bbad235] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 05/30/2023] [Accepted: 06/05/2023] [Indexed: 07/05/2023] Open
Abstract
Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect of one drug to the presence of another drug in the human body, which plays an essential role in drug discovery and clinical research. DDIs prediction through traditional clinical trials and experiments is an expensive and time-consuming process. To correctly apply the advanced AI and deep learning, the developer and user meet various challenges such as the availability and encoding of data resources, and the design of computational methods. This review summarizes chemical structure based, network based, natural language processing based and hybrid methods, providing an updated and accessible guide to the broad researchers and development community with different domain knowledge. We introduce widely used molecular representation and describe the theoretical frameworks of graph neural network models for representing molecular structures. We present the advantages and disadvantages of deep and graph learning methods by performing comparative experiments. We discuss the potential technical challenges and highlight future directions of deep and graph learning models for accelerating DDIs prediction.
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Affiliation(s)
- Xuan Lin
- College of Computer Science, Xiangtan University, Xiangtan, China
| | - Lichang Dai
- College of Computer Science, Xiangtan University, Xiangtan, China
| | - Yafang Zhou
- College of Computer Science, Xiangtan University, Xiangtan, China
| | - Zu-Guo Yu
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, China
| | - Wen Zhang
- College of Informatics, Huazhong Agricultural University, China
| | - Jian-Yu Shi
- Northwestern Polytechnical University, Xian, China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, China
| | - Li Zeng
- AIDD department of Yuyao Biotech, Shanghai, China
| | - Haowen Chen
- College of Computer Science and Electronic Engineering, Hunan University, 410013 Changsha, P. R. China
| | - Bosheng Song
- College of Information Science and Engineering, Hunan University, Changsha, China
| | - Philip S Yu
- University of Illinois at Chicago and also holds the Wexler Chair in Information Technology
| | - Xiangxiang Zeng
- College of Information Science and Engineering, Hunan University, Changsha, China
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33
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Bao X, Sun J, Yi M, Qiu J, Chen X, Shuai SC, Zhao Q. MPFFPSDC: A multi-pooling feature fusion model for predicting synergistic drug combinations. Methods 2023:S1046-2023(23)00098-1. [PMID: 37321525 DOI: 10.1016/j.ymeth.2023.06.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/11/2023] [Accepted: 06/12/2023] [Indexed: 06/17/2023] Open
Abstract
Drug combination therapies are common practice in the treatment of cancer, but not all combinations result in synergy. As traditional screening approaches are restricted in their ability to uncover synergistic drug combinations, computer-aided medicine is becoming a increasingly prevalent in this field. In this work, a predictive model of potential interactions between drugs named MPFFPSDC is presented, which can maintain the symmetry of drug inputs and eliminate inconsistencies in predictive results caused by different drug inputting sequences or positions. The experimental results show that MPFFPSDC outperforms comparative models in major performance indicators and exhibits better generalization for independent data. Furthermore, the case study demonstrates that our model can capture molecular substructures that contribute to the synergistic effect of two drugs. These results indicate that MPFFPSDC not only offers strong predictive performance, but also has good model interpretability that may provide new insights for the study of drug interaction mechanisms and the development of new drugs.
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Affiliation(s)
- Xin Bao
- School of Automation and Electrical Engineering, Linyi University, Linyi 276000, China
| | - Jianqiang Sun
- School of Automation and Electrical Engineering, Linyi University, Linyi 276000, China.
| | - Ming Yi
- School of Mathematics and Physics, China University of Geosciences, Wuhan 430000, China
| | - Jianlong Qiu
- School of Automation and Electrical Engineering, Linyi University, Linyi 276000, China
| | - Xiangyong Chen
- School of Automation and Electrical Engineering, Linyi University, Linyi 276000, China
| | - Stella C Shuai
- Biological Science, Northwestern University, Evanston, IL 60208, USA
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan 114051, China.
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34
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Zhang M, Gao H, Liao X, Ning B, Gu H, Yu B. DBGRU-SE: predicting drug-drug interactions based on double BiGRU and squeeze-and-excitation attention mechanism. Brief Bioinform 2023:7176312. [PMID: 37225428 DOI: 10.1093/bib/bbad184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 04/03/2023] [Accepted: 04/23/2023] [Indexed: 05/26/2023] Open
Abstract
The prediction of drug-drug interactions (DDIs) is essential for the development and repositioning of new drugs. Meanwhile, they play a vital role in the fields of biopharmaceuticals, disease diagnosis and pharmacological treatment. This article proposes a new method called DBGRU-SE for predicting DDIs. Firstly, FP3 fingerprints, MACCS fingerprints, Pubchem fingerprints and 1D and 2D molecular descriptors are used to extract the feature information of the drugs. Secondly, Group Lasso is used to remove redundant features. Then, SMOTE-ENN is applied to balance the data to obtain the best feature vectors. Finally, the best feature vectors are fed into the classifier combining BiGRU and squeeze-and-excitation (SE) attention mechanisms to predict DDIs. After applying five-fold cross-validation, The ACC values of DBGRU-SE model on the two datasets are 97.51 and 94.98%, and the AUC are 99.60 and 98.85%, respectively. The results showed that DBGRU-SE had good predictive performance for drug-drug interactions.
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Affiliation(s)
| | - Hongli Gao
- Qingdao University of Science and Technology, China
| | - Xin Liao
- Qingdao University of Science and Technology, China
| | - Baoxing Ning
- Qingdao University of Science and Technology, China
| | - Haiming Gu
- Qingdao University of Science and Technology, China
| | - Bin Yu
- Qingdao University of Science and Technology, China
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35
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Kha QH, Le VH, Hung TNK, Nguyen NTK, Le NQK. Development and Validation of an Explainable Machine Learning-Based Prediction Model for Drug-Food Interactions from Chemical Structures. SENSORS (BASEL, SWITZERLAND) 2023; 23:3962. [PMID: 37112302 PMCID: PMC10143839 DOI: 10.3390/s23083962] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 03/26/2023] [Accepted: 04/12/2023] [Indexed: 06/19/2023]
Abstract
Possible drug-food constituent interactions (DFIs) could change the intended efficiency of particular therapeutics in medical practice. The increasing number of multiple-drug prescriptions leads to the rise of drug-drug interactions (DDIs) and DFIs. These adverse interactions lead to other implications, e.g., the decline in medicament's effect, the withdrawals of various medications, and harmful impacts on the patients' health. However, the importance of DFIs remains underestimated, as the number of studies on these topics is constrained. Recently, scientists have applied artificial intelligence-based models to study DFIs. However, there were still some limitations in data mining, input, and detailed annotations. This study proposed a novel prediction model to address the limitations of previous studies. In detail, we extracted 70,477 food compounds from the FooDB database and 13,580 drugs from the DrugBank database. We extracted 3780 features from each drug-food compound pair. The optimal model was eXtreme Gradient Boosting (XGBoost). We also validated the performance of our model on one external test set from a previous study which contained 1922 DFIs. Finally, we applied our model to recommend whether a drug should or should not be taken with some food compounds based on their interactions. The model can provide highly accurate and clinically relevant recommendations, especially for DFIs that may cause severe adverse events and even death. Our proposed model can contribute to developing more robust predictive models to help patients, under the supervision and consultants of physicians, avoid DFI adverse effects in combining drugs and foods for therapy.
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Affiliation(s)
- Quang-Hien Kha
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
- AIBioMed Research Group, Taipei Medical University, Taipei 110, Taiwan
| | - Viet-Huan Le
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
- AIBioMed Research Group, Taipei Medical University, Taipei 110, Taiwan
- Department of Thoracic Surgery, Khanh Hoa General Hospital, Nha Trang City 65000, Vietnam
| | | | - Ngan Thi Kim Nguyen
- Undergraduate Program of Nutrition Science, National Taiwan Normal University, Taipei 106, Taiwan
| | - Nguyen Quoc Khanh Le
- AIBioMed Research Group, Taipei Medical University, Taipei 110, Taiwan
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei 110, Taiwan
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan
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36
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Yang Z, Tong K, Jin S, Wang S, Yang C, Jiang F. CNN-Siam: multimodal siamese CNN-based deep learning approach for drug‒drug interaction prediction. BMC Bioinformatics 2023; 24:110. [PMID: 36959539 PMCID: PMC10037822 DOI: 10.1186/s12859-023-05242-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 03/20/2023] [Indexed: 03/25/2023] Open
Abstract
BACKGROUND Drug‒drug interactions (DDIs) are reactions between two or more drugs, i.e., possible situations that occur when two or more drugs are used simultaneously. DDIs act as an important link in both drug development and clinical treatment. Since it is not possible to study the interactions of such a large number of drugs using experimental means, a computer-based deep learning solution is always worth investigating. We propose a deep learning-based model that uses twin convolutional neural networks to learn representations from multimodal drug data and to make predictions about the possible types of drug effects. RESULTS In this paper, we propose a novel convolutional neural network algorithm using a Siamese network architecture called CNN-Siam. CNN-Siam uses a convolutional neural network (CNN) as a backbone network in the form of a twin network architecture to learn the feature representation of drug pairs from multimodal data of drugs (including chemical substructures, targets and enzymes). Moreover, this network is used to predict the types of drug interactions with the best optimization algorithms available (RAdam and LookAhead). The experimental data show that the CNN-Siam achieves an area under the precision-recall (AUPR) curve score of 0.96 on the benchmark dataset and a correct rate of 92%. These results are significant improvements compared to the state-of-the-art method (from 86 to 92%) and demonstrate the robustness of the CNN-Siam and the superiority of the new optimization algorithm through ablation experiments. CONCLUSION The experimental results show that our multimodal siamese convolutional neural network can accurately predict DDIs, and the Siamese network architecture is able to learn the feature representation of drug pairs better than individual networks. CNN-Siam outperforms other state-of-the-art algorithms with the combination of data enhancement and better optimizers. But at the same time, CNN-Siam has some drawbacks, longer training time, generalization needs to be improved, and poorer classification results on some classes.
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Affiliation(s)
- Zihao Yang
- Faculty of Life Science and Food Engineering, Huaiyin Institute of Technology, Huaian, 223003, Jiangsu, China
| | - Kuiyuan Tong
- Faculty of Life Science and Food Engineering, Huaiyin Institute of Technology, Huaian, 223003, Jiangsu, China
| | - Shiyu Jin
- Faculty of Life Science and Food Engineering, Huaiyin Institute of Technology, Huaian, 223003, Jiangsu, China
| | - Shiyan Wang
- Faculty of Life Science and Food Engineering, Huaiyin Institute of Technology, Huaian, 223003, Jiangsu, China.
- Jiangsu Provincial Engineering Laboratory for Biomass Conversion and Process Integration, Huaiyin Institute of Technology, Huaian, 223003, Jiangsu, China.
| | - Chao Yang
- Translational Institute for Cancer Pain, Chongming Hospital Affiliated to Shanghai University of Health & Medicine Sciences (Xinhua Hospital Chongming Branch), Shanghai, 202150, China.
| | - Feng Jiang
- Translational Institute for Cancer Pain, Chongming Hospital Affiliated to Shanghai University of Health & Medicine Sciences (Xinhua Hospital Chongming Branch), Shanghai, 202150, China.
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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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38
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Wang L, Pan Z, Liu W, Wang J, Ji L, Shi D. A dual-attention based coupling network for diabetes classification with heterogeneous data. J Biomed Inform 2023; 139:104300. [PMID: 36736446 DOI: 10.1016/j.jbi.2023.104300] [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: 07/25/2022] [Revised: 12/02/2022] [Accepted: 01/26/2023] [Indexed: 02/05/2023]
Abstract
Diabetes Mellitus (DM) is a group of metabolic disorders characterized by hyperglycaemia in the absence of treatment. Classification of DM is essential as it corresponds to the respective diagnosis and treatment. In this paper, we propose a new coupling network with hierarchical dual-attention that utilizes heterogeneous data, including Flash Glucose Monitoring (FGM) data and biomarkers in electronic medical records. The long short-term memory-based FGM sub-network extracts the time-dependent features of dynamic FGM sequences, while the biomarkers sub-network learns the features of static biomarkers. The convolutional block attention module (CBAM) for dispersing the feature weights of the spatial and channel dimensions is implemented into the FGM sub-network to endure the variability of FGM and allows us to extract high-level discriminative features more accurately. To better adjust the importance weights of the characteristics of the two sub-networks, self-attention is introduced to integrate the characteristics of heterogeneous data. Based on the dataset provided by Peking University People's Hospital, the proposed method is evaluated through factorial experiments of multi-source heterogeneous data, ablation studies of various attention strategies, time consumption evaluation and quantitative evaluation. The benchmark tests reveal the proposed network achieves a type 1 and 2 diabetes classification accuracy of 95.835% and the comprehensive performance metrics, including Matthews correlation coefficient, F1-score and G-mean, are 91.333%, 94.939% and 94.937% respectively. In the factorial experiments, the proposed method reaches the maximum area under the receiver operating characteristic curve of 0.9428, which indicates the effectiveness of the coupling between the nominated sub-networks. The coupling network with a dual-attention strategy performs better than the one without or only with a single-attention strategy in the ablation study as well. In addition, the model is also tested on another data set, and the accuracy of the test reaches 94.286%, reflecting that the model is robust when it is transferred to untrained diabetes data. The experimental results show that the proposed method is feasible in the classification of diabetes types. The code is available at https://github.com/bitDalei/Diabetes-Classification-with-Heterogeneous-Data.
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Affiliation(s)
- Lei Wang
- Institute of Engineering Medicine, Beijing Institute of Technology, Beijing, China
| | - Zhenglin Pan
- Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China
| | - Wei Liu
- Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China.
| | - Junzheng Wang
- MIIT Key Laboratory of Servo Motion Systems Drive and Control, School of Automation, Beijing Institute of Technology, Beijing, China
| | - Linong Ji
- Department of Endocrinology and Metabolism, Peking University People's Hospital, Beijing, China
| | - Dawei Shi
- Institute of Engineering Medicine, Beijing Institute of Technology, Beijing, China; MIIT Key Laboratory of Servo Motion Systems Drive and Control, School of Automation, Beijing Institute of Technology, Beijing, China.
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MSEDDI: Multi-Scale Embedding for Predicting Drug-Drug Interaction Events. Int J Mol Sci 2023; 24:ijms24054500. [PMID: 36901929 PMCID: PMC10002564 DOI: 10.3390/ijms24054500] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 02/18/2023] [Accepted: 02/22/2023] [Indexed: 03/02/2023] Open
Abstract
A norm in modern medicine is to prescribe polypharmacy to treat disease. The core concern with the co-administration of drugs is that it may produce adverse drug-drug interaction (DDI), which can cause unexpected bodily injury. Therefore, it is essential to identify potential DDI. Most existing methods in silico only judge whether two drugs interact, ignoring the importance of interaction events to study the mechanism implied in combination drugs. In this work, we propose a deep learning framework named MSEDDI that comprehensively considers multi-scale embedding representations of the drug for predicting drug-drug interaction events. In MSEDDI, we design three-channel networks to process biomedical network-based knowledge graph embedding, SMILES sequence-based notation embedding, and molecular graph-based chemical structure embedding, respectively. Finally, we fuse three heterogeneous features from channel outputs through a self-attention mechanism and feed them to the linear layer predictor. In the experimental section, we evaluate the performance of all methods on two different prediction tasks on two datasets. The results show that MSEDDI outperforms other state-of-the-art baselines. Moreover, we also reveal the stable performance of our model in a broader sample set via case studies.
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40
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Wang T, Sun J, Zhao Q. Investigating cardiotoxicity related with hERG channel blockers using molecular fingerprints and graph attention mechanism. Comput Biol Med 2023; 153:106464. [PMID: 36584603 DOI: 10.1016/j.compbiomed.2022.106464] [Citation(s) in RCA: 112] [Impact Index Per Article: 112.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 12/12/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
Human ether-a-go-go-related gene (hERG) channel blockade by small molecules is a big concern during drug development in the pharmaceutical industry. Failure or inhibition of hERG channel activity caused by drug molecules can lead to prolonging QT interval, which will result in serious cardiotoxicity. Thus, evaluating the hERG blocking activity of all these small molecular compounds is technically challenging, and the relevant procedures are expensive and time-consuming. In this study, we develop a novel deep learning predictive model named DMFGAM for predicting hERG blockers. In order to characterize the molecule more comprehensively, we first consider the fusion of multiple molecular fingerprint features to characterize its final molecular fingerprint features. Then, we use the multi-head attention mechanism to extract the molecular graph features. Both molecular fingerprint features and molecular graph features are fused as the final features of the compounds to make the feature expression of compounds more comprehensive. Finally, the molecules are classified into hERG blockers or hERG non-blockers through the fully connected neural network. We conduct 5-fold cross-validation experiment to evaluate the performance of DMFGAM, and verify the robustness of DMFGAM on external validation datasets. We believe DMFGAM can serve as a powerful tool to predict hERG channel blockers in the early stages of drug discovery and development.
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Affiliation(s)
- Tianyi Wang
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China
| | - Jianqiang Sun
- School of Automation and Electrical Engineering, Linyi University, Linyi, 276000, China
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China.
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41
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Huang D, He H, Ouyang J, Zhao C, Dong X, Xie J. Small molecule drug and biotech drug interaction prediction based on multi-modal representation learning. BMC Bioinformatics 2022; 23:561. [PMID: 36575376 PMCID: PMC9793529 DOI: 10.1186/s12859-022-05101-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 12/06/2022] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Drug-drug interactions (DDIs) occur when two or more drugs are taken simultaneously or successively. Early detection of adverse drug interactions can be essential in preventing medical errors and reducing healthcare costs. Many computational methods already predict interactions between small molecule drugs (SMDs). As the number of biotechnology drugs (BioDs) increases, so makes the threat of interactions between SMDs and BioDs. However, few computational methods are available to predict their interactions. RESULTS Considering the structural specificity and relational complexity of SMDs and BioDs, a novel multi-modal representation learning method called Multi-SBI is proposed to predict their interactions. First, multi-modal features are used to adequately represent the heterogeneous structure and complex relationships of SMDs and BioDs. Second, an undersampling method based on Positive-unlabeled learning (PU-sampling) is introduced to obtain negative samples with high confidence from the unlabeled data set. Finally, both learned representations of SMD and BioD are fed into DNN classifiers to predict their interaction events. In addition, we also conduct a retrospective analysis. CONCLUSIONS Our proposed multi-modal representation learning method can extract drug features more comprehensively in heterogeneous drugs. In addition, PU-sampling can effectively reduce the noise in the sampling procedure. Our proposed method significantly outperforms other state-of-the-art drug interaction prediction methods. In a retrospective analysis of DrugBank 5.1.0, 14 out of the 20 predictions with the highest confidence were validated in the latest version of DrugBank 5.1.8, demonstrating that Multi-SBI is a valuable tool for predicting new drug interactions through effectively extracting and learning heterogeneous drug features.
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Affiliation(s)
- Dingkai Huang
- grid.39436.3b0000 0001 2323 5732School of Computer Engineering and Science, Shanghai University, Shanghai, 200444 China
| | - Hongjian He
- grid.39436.3b0000 0001 2323 5732School of Computer Engineering and Science, Shanghai University, Shanghai, 200444 China
| | - Jiaming Ouyang
- grid.39436.3b0000 0001 2323 5732School of Computer Engineering and Science, Shanghai University, Shanghai, 200444 China
| | - Chang Zhao
- grid.39436.3b0000 0001 2323 5732School of Computer Engineering and Science, Shanghai University, Shanghai, 200444 China
| | - Xin Dong
- grid.39436.3b0000 0001 2323 5732School of Medicine, Shanghai University, Shanghai, 200444 China
| | - Jiang Xie
- grid.39436.3b0000 0001 2323 5732School of Computer Engineering and Science, Shanghai University, Shanghai, 200444 China
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42
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Pan D, Quan L, Jin Z, Chen T, Wang X, Xie J, Wu T, Lyu Q. Multisource Attention-Mechanism-Based Encoder-Decoder Model for Predicting Drug-Drug Interaction Events. J Chem Inf Model 2022; 62:6258-6270. [PMID: 36449561 DOI: 10.1021/acs.jcim.2c01112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
Many computational methods have been proposed to predict drug-drug interactions (DDIs), which can occur when combining drugs to treat various diseases, but most mainly utilize single-source features of drugs, which is inadequate for drug representation. To fill this gap, we propose two attention-mechanism-based encoder-decoder models that incorporate multisource information: one is MAEDDI, which can predict DDIs, and the other is MAEDDIE, which can make further DDI-associated event predictions for drug pairs with DDIs. To better express the drug feature, we used three encoding methods to encode the drugs, integrating the self-attention mechanism, cross-attention mechanism, and graph attention network to construct a multisource feature fusion network. Experiments showed that both MAEDDI and MAEDDIE performed better than some state-of-the-art methods in various validation attempts at different experimental tasks. The visualization analysis showed that the semantic features of drug pairs learned from our models had a good drug representation. In practice, MAEDDIE successfully screened 43 DDI events on favipiravir, an influenza antiviral drug, with a success rate of nearly 50%. Our model achieved competitive results, mainly owing to the design of sequence-based, structural, biochemical, and statistical multisource features. Moreover, different encoders constructed based on different features learn the interrelationship information between drug pairs, and the different representations of these drug pairs are incorporated to predict the target problem. All of these encoders were designed to better characterize the complex DDI relationships, allowing us to achieve high generalization in DDI and DDI-associated event predations.
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Affiliation(s)
- Deng Pan
- School of Computer Science and Technology, Soochow University, Suzhou215006, China
| | - Lijun Quan
- School of Computer Science and Technology, Soochow University, Suzhou215006, China.,Province Key Lab for Information Processing Technologies, Soochow University, Suzhou215006, China.,Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing210000, China
| | - Zhi Jin
- School of Computer Science and Technology, Soochow University, Suzhou215006, China
| | - Taoning Chen
- School of Computer Science and Technology, Soochow University, Suzhou215006, China
| | - Xuejiao Wang
- School of Computer Science and Technology, Soochow University, Suzhou215006, China
| | - Jingxin Xie
- School of Computer Science and Technology, Soochow University, Suzhou215006, China
| | - Tingfang Wu
- School of Computer Science and Technology, Soochow University, Suzhou215006, China.,Province Key Lab for Information Processing Technologies, Soochow University, Suzhou215006, China.,Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing210000, China
| | - Qiang Lyu
- School of Computer Science and Technology, Soochow University, Suzhou215006, China.,Province Key Lab for Information Processing Technologies, Soochow University, Suzhou215006, China.,Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing210000, China
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Lin S, Chen W, Chen G, Zhou S, Wei DQ, Xiong Y. MDDI-SCL: predicting multi-type drug-drug interactions via supervised contrastive learning. J Cheminform 2022; 14:81. [DOI: 10.1186/s13321-022-00659-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 11/05/2022] [Indexed: 11/16/2022] Open
Abstract
AbstractThe joint use of multiple drugs may cause unintended drug-drug interactions (DDIs) and result in adverse consequence to the patients. Accurate identification of DDI types can not only provide hints to avoid these accidental events, but also elaborate the underlying mechanisms by how DDIs occur. Several computational methods have been proposed for multi-type DDI prediction, but room remains for improvement in prediction performance. In this study, we propose a supervised contrastive learning based method, MDDI-SCL, implemented by three-level loss functions, to predict multi-type DDIs. MDDI-SCL is mainly composed of three modules: drug feature encoder and mean squared error loss module, drug latent feature fusion and supervised contrastive loss module, multi-type DDI prediction and classification loss module. The drug feature encoder and mean squared error loss module uses self-attention mechanism and autoencoder to learn drug-level latent features. The drug latent feature fusion and supervised contrastive loss module uses multi-scale feature fusion to learn drug pair-level latent features. The prediction and classification loss module predicts DDI types of each drug pair. We evaluate MDDI-SCL on three different tasks of two datasets. Experimental results demonstrate that MDDI-SCL achieves better or comparable performance as the state-of-the-art methods. Furthermore, the effectiveness of supervised contrastive learning is validated by ablation experiment, and the feasibility of MDDI-SCL is supported by case studies. The source codes are available at https://github.com/ShenggengLin/MDDI-SCL.
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Lin S, Zhang G, Wei DQ, Xiong Y. DeepPSE: Prediction of polypharmacy side effects by fusing deep representation of drug pairs and attention mechanism. Comput Biol Med 2022; 149:105984. [PMID: 35994933 DOI: 10.1016/j.compbiomed.2022.105984] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 07/17/2022] [Accepted: 08/14/2022] [Indexed: 11/18/2022]
Abstract
Polypharmacy (multiple use of drugs) is an effective strategy for combating complex or co-existing diseases. However, a major consequence of polypharmacy is a higher risk of adverse side effects due to drug-drug interactions, which are rare and observed in relatively small clinical testing. Thus, identification of polypharmacy side effects remains challenging. Here, we propose a deep learning-based method, DeepPSE, to predict polypharmacy side effects in an end-to-end way. DeepPSE is composed of two main modules. First, multiple types of neural networks are constructed and fused to learn the deep representation of a drug pair. Second, the encoder block of transformer that includes self-attention mechanism is built to get latent features, which are further fed into the fully connected layer to predict polypharmacy side effects of drug pairs. Further, DeepPSE is compared with five baseline or state-of-the-art methods on a benchmark dataset of 964 types of polypharmacy side effects across 63473 drug pairs. Experimental results demonstrate that DeepPSE achieves better performance than that of all five methods. The source codes and data are available at https://github.com/ShenggengLin/DeepPSE.
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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
| | - Guangwei Zhang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong, 510275, 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, Zhongguancun Scientific Park, Meixi, Nayang, Henan, 473006, China; Peng Cheng National Laboratory, Vanke Cloud City Phase I Building 8, Xili Street, Nanshan District, Shenzhen, Guangdong, 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, China.
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45
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Yang Z, Zhong W, Lv Q, Yu-Chian Chen C. Learning size-adaptive molecular substructures for explainable drug-drug interaction prediction by substructure-aware graph neural network. Chem Sci 2022; 13:8693-8703. [PMID: 35974769 PMCID: PMC9337739 DOI: 10.1039/d2sc02023h] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 07/06/2022] [Indexed: 01/03/2023] Open
Abstract
Drug-drug interactions (DDIs) can trigger unexpected pharmacological effects on the body, and the causal mechanisms are often unknown. Graph neural networks (GNNs) have been developed to better understand DDIs. However, identifying key substructures that contribute most to the DDI prediction is a challenge for GNNs. In this study, we presented a substructure-aware graph neural network, a message passing neural network equipped with a novel substructure attention mechanism and a substructure-substructure interaction module (SSIM) for DDI prediction (SA-DDI). Specifically, the substructure attention was designed to capture size- and shape-adaptive substructures based on the chemical intuition that the sizes and shapes are often irregular for functional groups in molecules. DDIs are fundamentally caused by chemical substructure interactions. Thus, the SSIM was used to model the substructure-substructure interactions by highlighting important substructures while de-emphasizing the minor ones for DDI prediction. We evaluated our approach in two real-world datasets and compared the proposed method with the state-of-the-art DDI prediction models. The SA-DDI surpassed other approaches on the two datasets. Moreover, the visual interpretation results showed that the SA-DDI was sensitive to the structure information of drugs and was able to detect the key substructures for DDIs. These advantages demonstrated that the proposed method improved the generalization and interpretation capability of DDI prediction modeling.
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Affiliation(s)
- Ziduo Yang
- Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University Shenzhen 510275 China +86 02039332153
| | - Weihe Zhong
- Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University Shenzhen 510275 China +86 02039332153
| | - Qiujie Lv
- Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University Shenzhen 510275 China +86 02039332153
| | - Calvin Yu-Chian Chen
- Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University Shenzhen 510275 China +86 02039332153
- Department of Medical Research, China Medical University Hospital Taichung 40447 Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University Taichung 41354 Taiwan
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46
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Zhang X, Wang G, Meng X, Wang S, Zhang Y, Rodriguez-Paton A, Wang J, Wang X. Molormer: a lightweight self-attention-based method focused on spatial structure of molecular graph for drug-drug interactions prediction. Brief Bioinform 2022; 23:6645994. [PMID: 35849817 DOI: 10.1093/bib/bbac296] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 06/20/2022] [Accepted: 06/20/2022] [Indexed: 11/14/2022] Open
Abstract
Multi-drug combinations for the treatment of complex diseases are gradually becoming an important treatment, and this type of treatment can take advantage of the synergistic effects among drugs. However, drug-drug interactions (DDIs) are not just all beneficial. Accurate and rapid identifications of the DDIs are essential to enhance the effectiveness of combination therapy and avoid unintended side effects. Traditional DDIs prediction methods use only drug sequence information or drug graph information, which ignores information about the position of atoms and edges in the spatial structure. In this paper, we propose Molormer, a method based on a lightweight attention mechanism for DDIs prediction. Molormer takes the two-dimension (2D) structures of drugs as input and encodes the molecular graph with spatial information. Besides, Molormer uses lightweight-based attention mechanism and self-attention distilling to process spatially the encoded molecular graph, which not only retains the multi-headed attention mechanism but also reduces the computational and storage costs. Finally, we use the Siamese network architecture to serve as the architecture of Molormer, which can make full use of the limited data to train the model for better performance and also limit the differences to some extent between networks dealing with drug features. Experiments show that our proposed method outperforms state-of-the-art methods in Accuracy, Precision, Recall and F1 on multi-label DDIs dataset. In the case study section, we used Molormer to make predictions of new interactions for the drugs Aliskiren, Selexipag and Vorapaxar and validated parts of the predictions. Code and models are available at https://github.com/IsXudongZhang/Molormer.
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Affiliation(s)
- Xudong Zhang
- College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China
| | - Gan Wang
- College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China
| | - Xiangyu Meng
- College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China
| | - Shuang Wang
- College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China
| | - Ying Zhang
- College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China
| | - Alfonso Rodriguez-Paton
- Department of Artificial Intelligence, Faculty of Computer Science, Polytechnical University of Madrid, Campus de Montegancedo, Boadilla del Monte 28660, Madrid, Spain
| | - Jianmin Wang
- The Interdisciplinary Graduate Program in Integrative Biotechnology and Translational Medicin, Yonsei University, Incheon 21983, Korea
| | - Xun Wang
- College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China
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Dai X, Shen L. Advances and Trends in Omics Technology Development. Front Med (Lausanne) 2022; 9:911861. [PMID: 35860739 PMCID: PMC9289742 DOI: 10.3389/fmed.2022.911861] [Citation(s) in RCA: 89] [Impact Index Per Article: 44.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 05/09/2022] [Indexed: 12/11/2022] Open
Abstract
The human history has witnessed the rapid development of technologies such as high-throughput sequencing and mass spectrometry that led to the concept of “omics” and methodological advancement in systematically interrogating a cellular system. Yet, the ever-growing types of molecules and regulatory mechanisms being discovered have been persistently transforming our understandings on the cellular machinery. This renders cell omics seemingly, like the universe, expand with no limit and our goal toward the complete harness of the cellular system merely impossible. Therefore, it is imperative to review what has been done and is being done to predict what can be done toward the translation of omics information to disease control with minimal cell perturbation. With a focus on the “four big omics,” i.e., genomics, transcriptomics, proteomics, metabolomics, we delineate hierarchies of these omics together with their epiomics and interactomics, and review technologies developed for interrogation. We predict, among others, redoxomics as an emerging omics layer that views cell decision toward the physiological or pathological state as a fine-tuned redox balance.
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Li S, Wang L, Meng J, Zhao Q, Zhang L, Liu H. De Novo design of potential inhibitors against SARS-CoV-2 Mpro. Comput Biol Med 2022; 147:105728. [PMID: 35763931 PMCID: PMC9197785 DOI: 10.1016/j.compbiomed.2022.105728] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 05/31/2022] [Accepted: 06/11/2022] [Indexed: 11/16/2022]
Abstract
The impact of the ravages of COVID-19 on people's lives is obvious, and the development of novel potential inhibitors against SARS-CoV-2 main protease (Mpro), which has been validated as a potential target for drug design, is urgently needed. This study developed a model named MproI-GEN, which can be used for the de novo design of potential Mpro inhibitors (MproIs) based on deep learning. The model was mainly composed of long-short term memory modules, and the last layer was re-trained with transfer learning. The validity (0.9248), novelty (0.9668), and uniqueness (0.0652) of the designed potential MproI library (PMproIL) were evaluated, and the results showed that MproI-GEN could be used to design structurally novel and reasonable molecules. Additionally, PMproIL was filtered based on machine learning models and molecular docking. After filtering, the potential MproIs were verified with molecular dynamics simulations to evaluate the binding stability levels of these MproIs and SARS-CoV-2 Mpro, thereby illustrating the inhibitory effects of the potential MproIs against Mpro. Two potential MproIs were proposed in this study. This study provides not only new possibilities for the development of COVID-19 drugs but also a complete pipeline for the discovery of novel lead compounds.
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Affiliation(s)
- Shimeng Li
- School of Life Science, Liaoning University, Shenyang, 110036, China
| | - Lianxin Wang
- School of Life Science, Liaoning University, Shenyang, 110036, China
| | - Jinhui Meng
- School of Life Science, Liaoning University, Shenyang, 110036, China
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China.
| | - Li Zhang
- School of Life Science, Liaoning University, Shenyang, 110036, China; Shenyang Key Laboratory of Computer Simulating and Information Processing of Bio-macromolecules, Shenyang, 110036, China.
| | - Hongsheng Liu
- Shenyang Key Laboratory of Computer Simulating and Information Processing of Bio-macromolecules, Shenyang, 110036, China; Research Center for Computer Simulating and Information Processing of Bio-macromolecules of Liaoning Province, Shenyang, 110036, China; Engineering Laboratory for Molecular Simulation and Designing of Drug Molecules of Liaoning, Shenyang, 110036, China; School of Pharmaceutical Sciences, Liaoning University, Shenyang, 110036, China.
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Wu B, Li L, Cui Y, Zheng K. Cross-Adversarial Learning for Molecular Generation in Drug Design. Front Pharmacol 2022; 12:827606. [PMID: 35126153 PMCID: PMC8815768 DOI: 10.3389/fphar.2021.827606] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 12/30/2021] [Indexed: 11/18/2022] Open
Abstract
Molecular generation is an important but challenging task in drug design, as it requires optimization of chemical compound structures as well as many complex properties. Most of the existing methods use deep learning models to generate molecular representations. However, these methods are faced with the problems of generation validity and semantic information of labels. Considering these challenges, we propose a cross-adversarial learning method for molecular generation, CRAG for short, which integrates both the facticity of VAE-based methods and the diversity of GAN-based methods to further exploit the complex properties of Molecules. To be specific, an adversarially regularized encoder-decoder is used to transform molecules from simplified molecular input linear entry specification (SMILES) into discrete variables. Then, the discrete variables are trained to predict property and generate adversarial samples through projected gradient descent with corresponding labels. Our CRAG is trained using an adversarial pattern. Extensive experiments on two widely used benchmarks have demonstrated the effectiveness of our proposed method on a wide spectrum of metrics. We also utilize a novel metric named Novel/Sample to measure the overall generation effectiveness of models. Therefore, CRAG is promising for AI-based molecular design in various chemical applications.
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Affiliation(s)
- Banghua Wu
- School of Cyber Science and Engineering, Sichuan University, Chengdu, China
| | - Linjie Li
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Yue Cui
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Kai Zheng
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
- *Correspondence: Kai Zheng,
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