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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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Asfand-E-Yar M, Hashir Q, Shah AA, Malik HAM, Alourani A, Khalil W. Multimodal CNN-DDI: using multimodal CNN for drug to drug interaction associated events. Sci Rep 2024; 14:4076. [PMID: 38374325 PMCID: PMC10876630 DOI: 10.1038/s41598-024-54409-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 02/12/2024] [Indexed: 02/21/2024] Open
Abstract
Drug-to-drug interaction (DDIs) occurs when a patient consumes multiple drugs. Therefore, it is possible that any medication can influence other drugs' effectiveness. The drug-to-drug interactions are detected based on the interactions of chemical substructures, targets, pathways, and enzymes; therefore, machine learning (ML) and deep learning (DL) techniques are used to find the associated DDI events. The DL model, i.e., Convolutional Neural Network (CNN), is used to analyze the DDI. DDI is based on the 65 different drug-associated events, which is present in the drug bank database. Our model uses the inputs, which are chemical structures (i.e., smiles of drugs), enzymes, pathways, and the target of the drug. Therefore, for the multi-model CNN, we use several layers, activation functions, and features of drugs to achieve better accuracy as compared to traditional prediction algorithms. We perform different experiments on various hyperparameters. We have also carried out experiments on various iterations of drug features in different sets. Our Multi-Modal Convolutional Neural Network - Drug to Drug Interaction (MCNN-DDI) model achieved an accuracy of 90.00% and an AUPR of 94.78%. The results showed that a combination of the drug's features (i.e., chemical substructure, target, and enzyme) performs better in DDIs-associated events prediction than other features.
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Affiliation(s)
- Muhammad Asfand-E-Yar
- Department of Computer Science, CoE-AI, Center of Excellence Artificial Intelligence, Bahria University, Islamabad, Pakistan
| | - Qadeer Hashir
- Department of Computer Science, CoE-AI, Center of Excellence Artificial Intelligence, Bahria University, Islamabad, Pakistan
| | - Asghar Ali Shah
- Department of Computer Science, Bahria University, Islamabad , Pakistan
| | | | - Abdullah Alourani
- Department of Management Information Systems and Production Management, College of Business and Economics, Qassim University, Buraydah 51452, Saudi Arabia.
| | - Waqar Khalil
- Department of Computer Science, CoE-AI, Center of Excellence Artificial Intelligence, Bahria University, Islamabad, Pakistan
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García-Sosa AT. Benford's Law and distributions for better drug design. Expert Opin Drug Discov 2024; 19:131-137. [PMID: 37921672 DOI: 10.1080/17460441.2023.2277342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Accepted: 10/26/2023] [Indexed: 11/04/2023]
Abstract
INTRODUCTION Modern drug discovery incorporates various tools and data, heralding the beginning of the data-driven drug design (DD) era. The distributions of chemical and physical data used for Artificial Intelligence (AI)/Machine Learning (ML) and to drive DD have thus become highly important to be understood and used effectively. AREAS COVERED The authors perform a comprehensive exploration of the statistical distributions driving the data-intensive era of drug discovery, including Benford's Law in AI/ML-based DD. EXPERT OPINION As the relevance of data-driven discovery escalates, we anticipate meticulous scrutiny of datasets utilizing principles like Benford's Law to enhance data integrity and guide efficient resource allocation and experimental planning. In this data-driven era of the pharmaceutical and medical industries, addressing critical aspects such as bias mitigation, algorithm effectiveness, data stewardship, effects, and fraud prevention are essential. Harnessing Benford's Law and other distributions and statistical tests in DD provides a potent strategy to detect data anomalies, fill data gaps, and enhance dataset quality. Benford's Law is a fast method for data integrity and quality of datasets, the backbone of AI/ML and other modeling approaches, proving very useful in the design process.
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Affiliation(s)
- Alfonso T García-Sosa
- Chair of Molecular Technology, Institute of Chemistry, University of Tartu, Tartu, Estonia
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Alqaissi E, Alotaibi F, Sher Ramzan M, Algarni A. Novel graph-based machine-learning technique for viral infectious diseases: application to influenza and hepatitis diseases. Ann Med 2024; 55:2304108. [PMID: 38242107 PMCID: PMC10802812 DOI: 10.1080/07853890.2024.2304108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 12/18/2023] [Indexed: 01/21/2024] Open
Abstract
BACKGROUND Most infectious diseases are caused by viruses, fungi, bacteria and parasites. Their ability to easily infect humans and trigger large-scale epidemics makes them a public health concern. Methods for early detection of these diseases have been developed; however, they are hindered by the absence of a unified, interoperable and reusable model. This study seeks to create a holistic and real-time model for swift, preliminary detection of infectious diseases using symptoms and additional clinical data. MATERIALS AND METHODS In this study, we present a medical knowledge graph (MKG) that leverages multiple data sources to analyse connections between different nodes. Medical ontologies were used to enhance the MKG. We applied various graph algorithms to extract key features. The performance of multiple machine-learning (ML) techniques for influenza and hepatitis detection was assessed, selecting multi-layer perceptron (MLP) and random forest (RF) models due to their superior outcomes. The hyperparameters of both graph-based ML models were automatically fine-tuned. RESULTS Both the graph-based MLP and RF models showcased the least loss and error rates, along with the most specific, accurate recall, precision and F1 scores. Their Matthews correlation coefficients were also optimal. When compared with existing ML techniques and findings from the literature, these graph-based ML models manifested superior detection accuracy. CONCLUSIONS The graph-based MLP and RF models effectively diagnosed influenza and hepatitis, respectively. This underlines the potential of graph data science in enhancing ML model performance and uncovering concealed relationships in the MKG.
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Affiliation(s)
- Eman Alqaissi
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
- Computer Science and Information Systems, The Applied College, King Khalid University, Abha, Saudi Arabia
| | - Fahd Alotaibi
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Muhammad Sher Ramzan
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
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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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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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7
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Lv Q, Zhou J, Yang Z, He H, Chen CYC. 3D graph neural network with few-shot learning for predicting drug-drug interactions in scaffold-based cold start scenario. Neural Netw 2023; 165:94-105. [PMID: 37276813 DOI: 10.1016/j.neunet.2023.05.039] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 05/15/2023] [Accepted: 05/19/2023] [Indexed: 06/07/2023]
Abstract
Understanding drug-drug interactions (DDI) of new drugs is critical for minimizing unexpected adverse drug reactions. The modeling of new drugs is called a cold start scenario. In this scenario, Only a few structural information or physicochemical information about new drug is available. The 3D conformation of drug molecules usually plays a crucial role in chemical properties compared to the 2D structure. 3D graph network with few-shot learning is a promising solution. However, the 3D heterogeneity of drug molecules and the discretization of atomic distributions lead to spatial confusion in few-shot learning. Here, we propose a 3D graph neural network with few-shot learning, Meta3D-DDI, to predict DDI events in cold start scenario. The 3DGNN ensures rotation and translation invariance by calculating atomic pairwise distances, and incorporates 3D structure and distance information in the information aggregation stage. The continuous filter interaction module can continuously simulate the filter to obtain the interaction between the target atom and other atoms. Meta3D-DDI further develops a FSL strategy based on bilevel optimization to transfer meta-knowledge for DDI prediction tasks from existing drugs to new drugs. In addition, the existing cold start setting may cause the scaffold structure information in the training set to leak into the test set. We design scaffold-based cold start scenario to ensure that the drug scaffolds in the training set and test set do not overlap. The extensive experiments demonstrate that our architecture achieves the SOTA performance for DDI prediction under scaffold-based cold start scenario on two real-world datasets. The visual experiment shows that Meta3D-DDI significantly improves the learning for DDI prediction of new drugs. We also demonstrate how Meta3D-DDI can reduce the amount of data required to make meaningful DDI predictions.
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Affiliation(s)
- Qiujie Lv
- School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China
| | - Jun Zhou
- School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China
| | - Ziduo Yang
- School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China
| | - Haohuai He
- School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China
| | - Calvin Yu-Chian Chen
- School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China; 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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8
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Sun L, Mi K, Hou Y, Hui T, Zhang L, Tao Y, Liu Z, Huang L. Pharmacokinetic and Pharmacodynamic Drug-Drug Interactions: Research Methods and Applications. Metabolites 2023; 13:897. [PMID: 37623842 PMCID: PMC10456269 DOI: 10.3390/metabo13080897] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 07/24/2023] [Accepted: 07/25/2023] [Indexed: 08/26/2023] Open
Abstract
Because of the high research and development cost of new drugs, the long development process of new drugs, and the high failure rate at later stages, combining past drugs has gradually become a more economical and attractive alternative. However, the ensuing problem of drug-drug interactions (DDIs) urgently need to be solved, and combination has attracted a lot of attention from pharmaceutical researchers. At present, DDI is often evaluated and investigated from two perspectives: pharmacodynamics and pharmacokinetics. However, in some special cases, DDI cannot be accurately evaluated from a single perspective. Therefore, this review describes and compares the current DDI evaluation methods based on two aspects: pharmacokinetic interaction and pharmacodynamic interaction. The methods summarized in this paper mainly include probe drug cocktail methods, liver microsome and hepatocyte models, static models, physiologically based pharmacokinetic models, machine learning models, in vivo comparative efficacy studies, and in vitro static and dynamic tests. This review aims to serve as a useful guide for interested researchers to promote more scientific accuracy and clinical practical use of DDI studies.
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Affiliation(s)
- Lei Sun
- National Reference Laboratory of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China; (L.S.); (K.M.); (Y.H.); (T.H.); (L.Z.); (Y.T.)
- MAO Key Laboratory for Detection of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China;
| | - Kun Mi
- National Reference Laboratory of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China; (L.S.); (K.M.); (Y.H.); (T.H.); (L.Z.); (Y.T.)
- MOA Laboratory for Risk Assessment of Quality and Safety of Livestock and Poultry Products, Huazhong Agricultural University, Wuhan 430000, China
| | - Yixuan Hou
- National Reference Laboratory of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China; (L.S.); (K.M.); (Y.H.); (T.H.); (L.Z.); (Y.T.)
- MAO Key Laboratory for Detection of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China;
| | - Tianyi Hui
- National Reference Laboratory of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China; (L.S.); (K.M.); (Y.H.); (T.H.); (L.Z.); (Y.T.)
- MAO Key Laboratory for Detection of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China;
| | - Lan Zhang
- National Reference Laboratory of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China; (L.S.); (K.M.); (Y.H.); (T.H.); (L.Z.); (Y.T.)
- MAO Key Laboratory for Detection of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China;
| | - Yanfei Tao
- National Reference Laboratory of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China; (L.S.); (K.M.); (Y.H.); (T.H.); (L.Z.); (Y.T.)
- MAO Key Laboratory for Detection of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China;
| | - Zhenli Liu
- MAO Key Laboratory for Detection of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China;
- MOA Laboratory for Risk Assessment of Quality and Safety of Livestock and Poultry Products, Huazhong Agricultural University, Wuhan 430000, China
| | - Lingli Huang
- National Reference Laboratory of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China; (L.S.); (K.M.); (Y.H.); (T.H.); (L.Z.); (Y.T.)
- MAO Key Laboratory for Detection of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan 430000, China;
- MOA Laboratory for Risk Assessment of Quality and Safety of Livestock and Poultry Products, Huazhong Agricultural University, Wuhan 430000, China
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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: 7] [Impact Index Per Article: 7.0] [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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10
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Wang J, Zhang S, Li R, Chen G, Yan S, Ma L. Multi-view feature representation and fusion for drug-drug interactions prediction. BMC Bioinformatics 2023; 24:93. [PMID: 36918766 PMCID: PMC10015807 DOI: 10.1186/s12859-023-05212-4] [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: 12/12/2022] [Accepted: 02/27/2023] [Indexed: 03/16/2023] Open
Abstract
BACKGROUND Drug-drug interactions (DDIs) prediction is vital for pharmacology and clinical application to avoid adverse drug reactions on patients. It is challenging because DDIs are related to multiple factors, such as genes, drug molecular structure, diseases, biological processes, side effects, etc. It is a crucial technology for Knowledge graph to present multi-relation among entities. Recently some existing graph-based computation models have been proposed for DDIs prediction and get good performance. However, there are still some challenges in the knowledge graph representation, which can extract rich latent features from drug knowledge graph (KG). RESULTS In this work, we propose a novel multi-view feature representation and fusion (MuFRF) architecture to realize DDIs prediction. It consists of two views of feature representation and a multi-level latent feature fusion. For the feature representation from the graph view and KG view, we use graph isomorphism network to map drug molecular structures and use RotatE to implement the vector representation on bio-medical knowledge graph, respectively. We design concatenate-level and scalar-level strategies in the multi-level latent feature fusion to capture latent features from drug molecular structure information and semantic features from bio-medical KG. And the multi-head attention mechanism achieves the optimization of features on binary and multi-class classification tasks. We evaluate our proposed method based on two open datasets in the experiments. Experiments indicate that MuFRF outperforms the classic and state-of-the-art models. CONCLUSIONS Our proposed model can fully exploit and integrate the latent feature from the drug molecular structure graph (graph view) and rich bio-medical knowledge graph (KG view). We find that a multi-view feature representation and fusion model can accurately predict DDIs. It may contribute to providing with some guidance for research and validation for discovering novel DDIs.
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Affiliation(s)
- Jing Wang
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China.,Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou, China
| | - Shuo Zhang
- School of Artificial Intelligence, Henan University, Zhengzhou, China
| | - Runzhi Li
- Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou, China.
| | - Gang Chen
- Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou, China
| | - Siyu Yan
- Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lihong Ma
- Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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11
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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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12
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Deng C, Peng J, Li S. Research on the state of blended learning among college students - A mixed-method approach. Front Psychol 2022; 13:1054137. [PMID: 36532971 PMCID: PMC9751935 DOI: 10.3389/fpsyg.2022.1054137] [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: 09/26/2022] [Accepted: 11/07/2022] [Indexed: 11/03/2023] Open
Abstract
In the wake of the COVID-19 pandemic in 2019, China's education leaders began to focus on and promote blended learning. The process is still in its infancy in Chinese colleges and universities, and its development remains a problem to be solved. By combining technology acceptance and student participation, this article proposes an analysis model for assessing the factors influencing blended learning. A questionnaire was designed and distributed, and 796 valid responses were collected. The mean and variance were used to examine the status of students' technology acceptance and satisfaction with blended learning. The t-test method was employed to analyze the gender differences between students in regard to the topic. The results show that: (1) students majoring in computer science view the factors as having a high level of influence in blended learning. (2) There are major variances regarding the perception of service quality between male and female computer science major students. There is no significant difference between them in terms of perceived usefulness, perceived ease of use, or computer self-efficacy. (3) There are considerable disparities in the skill involvement and participation of computer science major college students. The results show that the technology acceptance and participation of students determine the effect of blended learning. Based on these findings, this article provides theoretical and practical suggestions for the implementation of blended learning to improve its effect.
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Affiliation(s)
- Chao Deng
- Information Management and Information System, Guangdong University of Science and Technology, Dongguan, China
| | - Jiao Peng
- Information Management and Information System, Guangdong University of Science and Technology, Dongguan, China
| | - ShuFei Li
- School of Artificial Intelligence, Dongguan Polytechnic, Dongguan, China
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13
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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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14
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Ren S, Yu L, Gao L. Multidrug representation learning based on pretraining model and molecular graph for drug interaction and combination prediction. Bioinformatics 2022; 38:4387-4394. [PMID: 35904544 DOI: 10.1093/bioinformatics/btac538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 07/06/2022] [Accepted: 07/27/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Approaches for the diagnosis and treatment of diseases often adopt the multidrug therapy method because it can increase the efficacy or reduce the toxic side effects of drugs. Using different drugs simultaneously may trigger unexpected pharmacological effects. Therefore, efficient identification of drug interactions is essential for the treatment of complex diseases. Currently proposed calculation methods are often limited by the collection of redundant drug features, a small amount of labeled data and low model generalization capabilities. Meanwhile, there is also a lack of unique methods for multidrug representation learning, which makes it more difficult to take full advantage of the originally scarce data. RESULTS Inspired by graph models and pretraining models, we integrated a large amount of unlabeled drug molecular graph information and target information, then designed a pretraining framework, MGP-DR (Molecular Graph Pretraining for Drug Representation), specifically for drug pair representation learning. The model uses self-supervised learning strategies to mine the contextual information within and between drug molecules to predict drug-drug interactions and drug combinations. The results achieved promising performance across multiple metrics compared with other state-of-the-art methods. Our MGP-DR model can be used to provide a reliable candidate set for the combined use of multiple drugs. AVAILABILITY AND IMPLEMENTATION Code of the model, datasets and results can be downloaded from GitHub (https://github.com/LiangYu-Xidian/MGP-DR). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Shujie Ren
- School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
| | - Liang Yu
- School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
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15
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Jang HY, Song J, Kim JH, Lee H, Kim IW, Moon B, Oh JM. Machine learning-based quantitative prediction of drug exposure in drug-drug interactions using drug label information. NPJ Digit Med 2022; 5:88. [PMID: 35817846 PMCID: PMC9273620 DOI: 10.1038/s41746-022-00639-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 06/16/2022] [Indexed: 11/27/2022] Open
Abstract
Many machine learning techniques provide a simple prediction for drug-drug interactions (DDIs). However, a systematically constructed database with pharmacokinetic (PK) DDI information does not exist, nor is there a machine learning model that numerically predicts PK fold change (FC) with it. Therefore, we propose a PK DDI prediction (PK-DDIP) model for quantitative DDI prediction with high accuracy, while constructing a highly reliable PK-DDI database. Reliable information of 3,627 PK DDIs was constructed from 3,587 drugs using 38,711 Food and Drug Administration (FDA) drug labels. This PK-DDIP model predicted the FC of the area under the time-concentration curve (AUC) within ± 0.5959. The prediction proportions within 0.8–1.25-fold, 0.67–1.5-fold, and 0.5–2-fold of the AUC were 75.77, 86.68, and 94.76%, respectively. Two external validations confirmed good prediction performance for newly updated FDA labels and FC from patients’. This model enables potential DDI evaluation before clinical trials, which will save time and cost.
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Affiliation(s)
- Ha Young Jang
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, Republic of Korea
| | - Jihyeon Song
- Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea
| | - Jae Hyun Kim
- School of Pharmacy, Jeonbuk National University, Jeonju, Republic of Korea
| | - Howard Lee
- Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul, Korea
| | - In-Wha Kim
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, Republic of Korea
| | - Bongki Moon
- Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea.
| | - Jung Mi Oh
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, Republic of Korea.
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16
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Liu Q, Yao E, Liu C, Zhou X, Li Y, Xu M. M2GCN: multi-modal graph convolutional network for modeling polypharmacy side effects. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03839-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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17
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Zhang Y, Li Z, Duan B, Qin L, Peng J. MKGE: Knowledge Graph Embedding with Molecular Structure Information. Comput Biol Chem 2022; 100:107730. [DOI: 10.1016/j.compbiolchem.2022.107730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 07/12/2022] [Indexed: 11/30/2022]
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18
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Chen M, Jiang W, Pan Y, Dai J, Lei Y, Ji C. SGFNNs: Signed Graph Filtering-based Neural Networks for Predicting Drug-Drug Interactions. J Comput Biol 2022; 29:1104-1116. [PMID: 35723646 DOI: 10.1089/cmb.2022.0113] [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: 11/13/2022] Open
Abstract
Capturing comprehensive information about drug-drug interactions (DDIs) is one of the key tasks in public health and drug development. Recently, graph neural networks (GNNs) have received increasing attention in the drug discovery domain due to their capability of integrating drugs profiles and the network structure into a low-dimensional feature space for predicting links and classification. Most of GNN models for DDI predictions are built on an unsigned graph, which tends to represent associated nodes with similar embedding results. However, semantic correlation between drugs, such as degressive effects, or even adverse side reactions should be disassortative. In this study, we put forward signed GNNs to model assortative and disassortative relationships within drug pairs. Since negative links exclude direct generalization of spectral filters on unsigned graph, we divide the signed graph into two unsigned subgraphs to dedicate two spectral filters, which captures both commonality and difference of drug pairs. For drug representations we derive two signed graph filtering-based neural networks (SGFNNs) which integrate signed graph structures and drug node attributes. Moreover, we use an end-to-end framework for learning DDIs, where an SGFNN together with a discriminator is jointly trained under a problem-specific loss function. The experimental results on two prediction problems show that our framework can obtain significant improvements compared with baselines. The case study further verifies the validation of our method.
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Affiliation(s)
- Ming Chen
- Department of Artificial Intelligence, College of Information Science and Engineering, Hunan Normal University, Changsha, Hunan, China
| | - Wei Jiang
- Department of Artificial Intelligence, College of Information Science and Engineering, Hunan Normal University, Changsha, Hunan, China
| | - Yi Pan
- Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Jianhua Dai
- Department of Artificial Intelligence, College of Information Science and Engineering, Hunan Normal University, Changsha, Hunan, China
| | - Yunwen Lei
- School of Computer Science, University of Birmingham, Birmingham, United Kingdom
| | - Chunyan Ji
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
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19
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Wang S, Lin M, Ghosal T, Ding Y, Peng Y. Knowledge Graph Applications in Medical Imaging Analysis: A Scoping Review. HEALTH DATA SCIENCE 2022; 2022:9841548. [PMID: 35800847 PMCID: PMC9259200 DOI: 10.34133/2022/9841548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 05/22/2022] [Indexed: 11/06/2022]
Abstract
Background There is an increasing trend to represent domain knowledge in structured graphs, which provide efficient knowledge representations for many downstream tasks. Knowledge graphs are widely used to model prior knowledge in the form of nodes and edges to represent semantically connected knowledge entities, which several works have adopted into different medical imaging applications. Methods We systematically searched over five databases to find relevant articles that applied knowledge graphs to medical imaging analysis. After screening, evaluating, and reviewing the selected articles, we performed a systematic analysis. Results We looked at four applications in medical imaging analysis, including disease classification, disease localization and segmentation, report generation, and image retrieval. We also identified limitations of current work, such as the limited amount of available annotated data and weak generalizability to other tasks. We further identified the potential future directions according to the identified limitations, including employing semisupervised frameworks to alleviate the need for annotated data and exploring task-agnostic models to provide better generalizability. Conclusions We hope that our article will provide the readers with aggregated documentation of the state-of-the-art knowledge graph applications for medical imaging to encourage future research.
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Affiliation(s)
- Song Wang
- The University of Texas at Austin, Austin, USA
| | - Mingquan Lin
- Population Health Sciences, Weill Cornell Medicine, New York, USA
| | - Tirthankar Ghosal
- Institute of Formal and Applied Linguistics, Charles University, Czechia, Czech Republic
| | - Ying Ding
- The University of Texas at Austin, Austin, USA
| | - Yifan Peng
- Population Health Sciences, Weill Cornell Medicine, New York, USA
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20
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He C, Liu Y, Li H, Zhang H, Mao Y, Qin X, Liu L, Zhang X. Multi-type feature fusion based on graph neural network for drug-drug interaction prediction. BMC Bioinformatics 2022; 23:224. [PMID: 35689200 PMCID: PMC9188183 DOI: 10.1186/s12859-022-04763-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 04/26/2022] [Indexed: 11/28/2022] Open
Abstract
Background Drug-Drug interactions (DDIs) are a challenging problem in drug research. Drug combination therapy is an effective solution to treat diseases, but it can also cause serious side effects. Therefore, DDIs prediction is critical in pharmacology. Recently, researchers have been using deep learning techniques to predict DDIs. However, these methods only consider single information of the drug and have shortcomings in robustness and scalability. Results In this paper, we propose a multi-type feature fusion based on graph neural network model (MFFGNN) for DDI prediction, which can effectively fuse the topological information in molecular graphs, the interaction information between drugs and the local chemical context in SMILES sequences. In MFFGNN, to fully learn the topological information of drugs, we propose a novel feature extraction module to capture the global features for the molecular graph and the local features for each atom of the molecular graph. In addition, in the multi-type feature fusion module, we use the gating mechanism in each graph convolution layer to solve the over-smoothing problem during information delivery. We perform extensive experiments on multiple real datasets. The results show that MFFGNN outperforms some state-of-the-art models for DDI prediction. Moreover, the cross-dataset experiment results further show that MFFGNN has good generalization performance. Conclusions Our proposed model can efficiently integrate the information from SMILES sequences, molecular graphs and drug-drug interaction networks. We find that a multi-type feature fusion model can accurately predict DDIs. It may contribute to discovering novel DDIs.
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Affiliation(s)
- Changxiang He
- College of Science, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Yuru Liu
- College of Science, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Hao Li
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Hui Zhang
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Yaping Mao
- School of Mathematics and Statistis, Qinghai Normal University, Xining, 810008, China
| | - Xiaofei Qin
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Lele Liu
- College of Science, University of Shanghai for Science and Technology, Shanghai, 200093, China.
| | - Xuedian Zhang
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
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21
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Yao J, Sun W, Jian Z, Wu Q, Wang X. Effective knowledge graph embeddings based on multidirectional semantics relations for polypharmacy side effects prediction. Bioinformatics 2022; 38:2315-2322. [PMID: 35176135 DOI: 10.1093/bioinformatics/btac094] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 01/25/2022] [Accepted: 02/15/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Polypharmacy is the combined use of drugs for the treatment of diseases. However, it often shows a high risk of side effects. Due to unnecessary interactions of combined drugs, the side effects of polypharmacy increase the risk of disease and even lead to death. Thus, obtaining abundant and comprehensive information on the side effects of polypharmacy is a vital task in the healthcare industry. Early traditional methods used machine learning techniques to predict side effects. However, they often make costly efforts to extract features of drugs for prediction. Later, several methods based on knowledge graphs are proposed. They are reported to outperform traditional methods. However, they still show limited performance by failing to model complex relations of side effects among drugs. RESULTS To resolve the above problems, we propose a novel model by further incorporating complex relations of side effects into knowledge graph embeddings. Our model can translate and transmit multidirectional semantics with fewer parameters, leading to better scalability in large-scale knowledge graphs. Experimental evaluation shows that our model outperforms state-of-the-art models in terms of the average area under the ROC and precision-recall curves. AVAILABILITY AND IMPLEMENTATION Code and data are available at: https://github.com/galaxysunwen/MSTE-master.
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Affiliation(s)
- Junfeng Yao
- School of Informatics, Xiamen University, Xiamen, Fujian 361005, China.,Institute of Artificial Intelligence, Xiamen University, Xiamen, Fujian 361005, China.,Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism, Xiamen University, Xiamen, Fujian 361005, China
| | - Wen Sun
- School of Informatics, Xiamen University, Xiamen, Fujian 361005, China
| | - Zhongquan Jian
- School of Informatics, Xiamen University, Xiamen, Fujian 361005, China.,Institute of Artificial Intelligence, Xiamen University, Xiamen, Fujian 361005, China
| | - Qingqiang Wu
- School of Informatics, Xiamen University, Xiamen, Fujian 361005, China.,Institute of Artificial Intelligence, Xiamen University, Xiamen, Fujian 361005, China.,Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism, Xiamen University, Xiamen, Fujian 361005, China
| | - Xiaoli Wang
- School of Informatics, Xiamen University, Xiamen, Fujian 361005, China
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22
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Hao X, Chen Q, Pan H, Qiu J, Zhang Y, Yu Q, Han Z, Du X. Enhancing drug-drug interaction prediction by three-way decision and knowledge graph embedding. GRANULAR COMPUTING 2022; 8:67-76. [PMID: 38624759 PMCID: PMC8913867 DOI: 10.1007/s41066-022-00315-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 02/15/2022] [Indexed: 11/30/2022]
Abstract
Drug-Drug interaction (DDI) prediction is essential in pharmaceutical research and clinical application. Existing computational methods mainly extract data from multiple resources and treat it as binary classification. However, this cannot unambiguously tell the boundary between positive and negative samples owing to the incompleteness and uncertainty of derived data. A granular computing method called three-way decision is proved to be effective in making uncertain decision, but it relies on supplementary information to make delay decision. Recently, biomedical knowledge graph has been regarded as an important source to obtain abundant supplementary information about drugs. This paper proposes a three-way decision-based method called 3WDDI, in combination with knowledge graph embedding as supplementary features to enhance DDI prediction. The drug pairs are divided into positive, negative and boundary regions by Convolutional Neural Network (CNN) according to drug chemical structure feature. Further, delay decision is made for objects in the boundary region by integrating knowledge graph embedding feature to promote the accuracy of decision-making. The empirical results show that 3WDDI yields up to 0.8922, 0.9614, 0.9582, 0.8930 for Accuracy, AUPR, AUC and F1-score, respectively, and outperforms several baseline models.
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Affiliation(s)
- Xinkun Hao
- School of Computer, Electronics and Information, Guangxi University, Nanning, 530004 Guangxi China
- School of Computer Science and Engineering, Yulin Normal University, Yulin, 537000 Guangxi China
| | - Qingfeng Chen
- School of Computer, Electronics and Information, Guangxi University, Nanning, 530004 Guangxi China
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIA 3086 Australia
- School of Computer Science and Engineering, Yulin Normal University, Yulin, 537000 Guangxi China
| | - Haiming Pan
- School of Computer, Electronics and Information, Guangxi University, Nanning, 530004 Guangxi China
- School of Computer Science and Engineering, Yulin Normal University, Yulin, 537000 Guangxi China
| | - Jie Qiu
- School of Computer, Electronics and Information, Guangxi University, Nanning, 530004 Guangxi China
- School of Computer Science and Engineering, Yulin Normal University, Yulin, 537000 Guangxi China
| | - Yuxiao Zhang
- School of Computer, Electronics and Information, Guangxi University, Nanning, 530004 Guangxi China
- School of Computer Science and Engineering, Yulin Normal University, Yulin, 537000 Guangxi China
| | - Qian Yu
- School of Computer, Electronics and Information, Guangxi University, Nanning, 530004 Guangxi China
- School of Computer Science and Engineering, Yulin Normal University, Yulin, 537000 Guangxi China
| | - Zongzhao Han
- School of Computer, Electronics and Information, Guangxi University, Nanning, 530004 Guangxi China
- School of Computer Science and Engineering, Yulin Normal University, Yulin, 537000 Guangxi China
| | - Xiaojing Du
- School of Computer, Electronics and Information, Guangxi University, Nanning, 530004 Guangxi China
- School of Computer Science and Engineering, Yulin Normal University, Yulin, 537000 Guangxi China
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23
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Han X, Xie R, Li X, Li J. SmileGNN: Drug–Drug Interaction Prediction Based on the SMILES and Graph Neural Network. Life (Basel) 2022; 12:life12020319. [PMID: 35207606 PMCID: PMC8879716 DOI: 10.3390/life12020319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 12/27/2021] [Accepted: 01/05/2022] [Indexed: 11/16/2022] Open
Abstract
Concurrent use of multiple drugs can lead to unexpected adverse drug reactions. The interaction between drugs can be confirmed by routine in vitro and clinical trials. However, it is difficult to test the drug–drug interactions widely and effectively before the drugs enter the market. Therefore, the prediction of drug–drug interactions has become one of the research priorities in the biomedical field. In recent years, researchers have been using deep learning to predict drug–drug interactions by exploiting drug structural features and graph theory, and have achieved a series of achievements. A drug–drug interaction prediction model SmileGNN is proposed in this paper, which can be characterized by aggregating the structural features of drugs constructed by SMILES data and the topological features of drugs in knowledge graphs obtained by graph neural networks. The experimental results show that the model proposed in this paper combines a variety of data sources and has a better prediction performance compared with existing prediction models of drug–drug interactions. Five out of the top ten predicted new drug–drug interactions are verified from the latest database, which proves the credibility of SmileGNN.
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Affiliation(s)
- Xueting Han
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China; (X.H.); (X.L.)
| | - Ruixia Xie
- School of Medical Technology and Nursing, Shenzhen Polytechnic, Shenzhen 518055, China;
| | - Xutao Li
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China; (X.H.); (X.L.)
| | - Junyi Li
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China; (X.H.); (X.L.)
- Correspondence:
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24
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OUP accepted manuscript. Brief Funct Genomics 2022; 21:216-229. [DOI: 10.1093/bfgp/elac004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 02/03/2022] [Accepted: 03/01/2022] [Indexed: 11/14/2022] Open
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25
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Yu H, Shen ZA, Du PF. NPI-RGCNAE: Fast predicting ncRNA-protein interactions using the Relational Graph Convolutional Network Auto-Encoder. IEEE J Biomed Health Inform 2021; 26:1861-1871. [PMID: 34699377 DOI: 10.1109/jbhi.2021.3122527] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
- ncRNAs play important roles in a variety of biological processes by interacting with RNA-binding proteins. Therefore, identifying ncRNA-protein interactions is important to understanding the biological functions of ncRNAs. Since experimental methods to determine ncRNA-protein interactions are always costly and time-consuming, computational methods have been proposed as alternative approaches. We developed a novel method NPI-RGCNAE (predicting ncRNA-Protein Interactions by the Relational Graph Convolutional Network Auto-Encoder). With a reliable negative sample selection strategy, we applied the Relational Graph Convolutional Network encoder and the DistMult decoder to predict ncRNA-protein interactions in an accurate and efficient way. By using the 5-fold cross-validation, we found that our method achieved a comparable performance to all state-of-the-art methods. Our method requires less than 10% training time of all state-of-the-art methods. It is a more efficient choice with large datasets in practice. All datasets and source codes of NPI-RGCNAE have been deposited in a public Github repository (https://github.com/Angelia0hh/NPI-RGCNAE).
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26
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Zeng X, Tu X, Liu Y, Fu X, Su Y. Toward better drug discovery with knowledge graph. Curr Opin Struct Biol 2021; 72:114-126. [PMID: 34649044 DOI: 10.1016/j.sbi.2021.09.003] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 08/18/2021] [Accepted: 09/06/2021] [Indexed: 01/08/2023]
Abstract
Drug discovery is the process of new drug identification. This process is driven by the increasing data from existing chemical libraries and data banks. The knowledge graph is introduced to the domain of drug discovery for imposing an explicit structure to integrate heterogeneous biomedical data. The graph can provide structured relations among multiple entities and unstructured semantic relations associated with entities. In this review, we summarize knowledge graph-based works that implement drug repurposing and adverse drug reaction prediction for drug discovery. As knowledge representation learning is a common way to explore knowledge graphs for prediction problems, we introduce several representative embedding models to provide a comprehensive understanding of knowledge representation learning.
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Affiliation(s)
- Xiangxiang Zeng
- College of Information Science and Engineering, Hunan University, Changsha, 410086, China
| | - Xinqi Tu
- College of Information Science and Engineering, Hunan University, Changsha, 410086, China
| | - Yuansheng Liu
- College of Information Science and Engineering, Hunan University, Changsha, 410086, China.
| | - Xiangzheng Fu
- College of Information Science and Engineering, Hunan University, Changsha, 410086, China
| | - Yansen Su
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei, 230601, China
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