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Han J, Kang MJ, Lee S. DRSPRING: Graph convolutional network (GCN)-Based drug synergy prediction utilizing drug-induced gene expression profile. Comput Biol Med 2024; 174:108436. [PMID: 38643597 DOI: 10.1016/j.compbiomed.2024.108436] [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: 01/25/2024] [Revised: 04/01/2024] [Accepted: 04/07/2024] [Indexed: 04/23/2024]
Abstract
Great efforts have been made over the years to identify novel drug pairs with synergistic effects. Although numerous computational approaches have been proposed to analyze diverse types of biological big data, the pharmacogenomic profiles, presumably the most direct proxy of drug effects, have been rarely used due to the data sparsity problem. In this study, we developed a composite deep-learning-based model that predicts the drug synergy effect utilizing pharmacogenomic profiles as well as molecular properties. Graph convolutional network (GCN) was used to represent and integrate the chemical structure, genetic interactions, drug-target information, and gene expression profiles of cell lines. Insufficient amount of pharmacogenomic data, i.e., drug-induced expression profiles from the LINCS project, was resolved by augmenting the data with the predicted profiles. Our method learned and predicted the Loewe synergy score in the DrugComb database and achieved a better or comparable performance compared to other published methods in a benchmark test. We also investigated contribution of various input features, which highlighted the value of basal gene expression and pharmacogenomic profiles of each cell line. Importantly, DRSPRING (DRug Synergy PRediction by INtegrated GCN) can be applied to any drug pairs and any cell lines, greatly expanding its applicability compared to previous methods.
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Affiliation(s)
- Jiyeon Han
- Department of Bio-Information Science, Ewha Womans University, Seoul, 03760, Republic of Korea
| | - Min Ji Kang
- Department of Life Sciences, Ewha Womans University, Seoul, 03760, Republic of Korea
| | - Sanghyuk Lee
- Department of Bio-Information Science, Ewha Womans University, Seoul, 03760, Republic of Korea; Department of Life Sciences, Ewha Womans University, Seoul, 03760, Republic of Korea.
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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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Kpanou R, Dallaire P, Rousseau E, Corbeil J. Learning self-supervised molecular representations for drug-drug interaction prediction. BMC Bioinformatics 2024; 25:47. [PMID: 38291362 PMCID: PMC10829170 DOI: 10.1186/s12859-024-05643-7] [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: 09/20/2023] [Accepted: 01/05/2024] [Indexed: 02/01/2024] Open
Abstract
Drug-drug interactions (DDI) are a critical concern in healthcare due to their potential to cause adverse effects and compromise patient safety. Supervised machine learning models for DDI prediction need to be optimized to learn abstract, transferable features, and generalize to larger chemical spaces, primarily due to the scarcity of high-quality labeled DDI data. Inspired by recent advances in computer vision, we present SMR-DDI, a self-supervised framework that leverages contrastive learning to embed drugs into a scaffold-based feature space. Molecular scaffolds represent the core structural motifs that drive pharmacological activities, making them valuable for learning informative representations. Specifically, we pre-trained SMR-DDI on a large-scale unlabeled molecular dataset. We generated augmented views for each molecule via SMILES enumeration and optimized the embedding process through contrastive loss minimization between views. This enables the model to capture relevant and robust molecular features while reducing noise. We then transfer the learned representations for the downstream prediction of DDI. Experiments show that the new feature space has comparable expressivity to state-of-the-art molecular representations and achieved competitive DDI prediction results while training on less data. Additional investigations also revealed that pre-training on more extensive and diverse unlabeled molecular datasets improved the model's capability to embed molecules more effectively. Our results highlight contrastive learning as a promising approach for DDI prediction that can identify potentially hazardous drug combinations using only structural information.
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Affiliation(s)
- Rogia Kpanou
- Département d'informatique et Génie Logiciel, Université Laval, Québec City, QC, Canada.
| | - Patrick Dallaire
- Département d'informatique et Génie Logiciel, Université Laval, Québec City, QC, Canada
| | - Elsa Rousseau
- Département d'informatique et Génie Logiciel, Université Laval, Québec City, QC, Canada
- Centre de Recherche en Données Massives de l'Université Laval, Québec City, QC, Canada
- Centre Nutrition, Santé et Société (NUTRISS), Institute of Nutrition and Functional Foods (INAF), Université Laval, Québec City, QC, Canada
| | - Jacques Corbeil
- Centre de Recherche en Données Massives de l'Université Laval, Québec City, QC, Canada.
- Centre de Recherche en Infectiologie de l'Université Laval, Axe Maladies Infectieuses et Immunitaires, Centre de Recherche du CHU de Québec-Université Laval, Québec City, QC, Canada.
- Département de Médecine Moléculaire, Faculté de Médecine, Université Laval, Québec City, QC, Canada.
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Kee G, Kang HJ, Ahn I, Gwon H, Kim Y, Seo H, Choi H, Cho HN, Kim M, Han J, Park S, Kim K, Jun TJ, Kim YH. Are polypharmacy side effects predicted by public data still valid in real-world data? Heliyon 2024; 10:e24620. [PMID: 38304832 PMCID: PMC10831713 DOI: 10.1016/j.heliyon.2024.e24620] [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: 10/24/2023] [Revised: 12/29/2023] [Accepted: 01/11/2024] [Indexed: 02/03/2024] Open
Abstract
Background and Objective Although interest in predicting drug-drug interactions is growing, many predictions are not verified by real-world data. This study aimed to confirm whether predicted polypharmacy side effects using public data also occur in data from actual patients. Methods We utilized a deep learning-based polypharmacy side effects prediction model to identify cefpodoxime-chlorpheniramine-lung edema combination with a high prediction score and a significant patient population. The retrospective study analyzed patients over 18 years old who were admitted to the Asan medical center between January 2000 and December 2020 and took cefpodoxime or chlorpheniramine orally. The three groups, cefpodoxime-treated, chlorpheniramine-treated, and cefpodoxime & chlorpheniramine-treated were compared using inverse probability of treatment weighting (IPTW) to balance them. Differences between the three groups were analyzed using the Kaplan-Meier method and Cox proportional hazards model. Results The study population comprised 54,043 patients with a history of taking cefpodoxime, 203,897 patients with a history of taking chlorpheniramine, and 1,628 patients with a history of taking cefpodoxime and chlorpheniramine simultaneously. After adjustment, the 1-year cumulative incidence of lung edema in the patient group that took cefpodoxime and chlorpheniramine simultaneously was significantly higher than in the patient groups that took cefpodoxime or chlorpheniramine only (p=0.001). Patients taking cefpodoxime and chlorpheniramine together had an increased risk of lung edema compared to those taking cefpodoxime alone [hazard ratio (HR) 2.10, 95% CI 1.26-3.52, p<0.005] and those taking chlorpheniramine alone, which also increased the risk of lung edema (HR 1.64, 95% CI 0.99-2.69, p=0.05). Conclusions Validation of polypharmacy side effect predictions with real-world data can aid patient and clinician decision-making before conducting randomized controlled trials. Simultaneous use of cefpodoxime and chlorpheniramine was associated with a higher long-term risk of lung edema compared to the use of cefpodoxime or chlorpheniramine alone.
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Affiliation(s)
- Gaeun Kee
- Department of Information Medicine, Asan Medical Center, 88, Olympicro 43gil, Songpagu, 05505, Seoul, Republic of Korea
| | - Hee Jun Kang
- Division of Cardiology, Asan Medical Center, 88, Olympicro 43gil, Songpagu, 05505, Seoul, Republic of Korea
| | - Imjin Ahn
- Department of Information Medicine, Asan Medical Center, 88, Olympicro 43gil, Songpagu, 05505, Seoul, Republic of Korea
| | - Hansle Gwon
- Department of Information Medicine, Asan Medical Center, 88, Olympicro 43gil, Songpagu, 05505, Seoul, Republic of Korea
| | - Yunha Kim
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43gil, Songpagu, 05505, Seoul, Republic of Korea
| | - Hyeram Seo
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43gil, Songpagu, 05505, Seoul, Republic of Korea
| | - Heejung Choi
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43gil, Songpagu, 05505, Seoul, Republic of Korea
| | - Ha Na Cho
- Department of Information Medicine, Asan Medical Center, 88, Olympicro 43gil, Songpagu, 05505, Seoul, Republic of Korea
| | - Minkyoung Kim
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43gil, Songpagu, 05505, Seoul, Republic of Korea
| | - JiYe Han
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43gil, Songpagu, 05505, Seoul, Republic of Korea
| | - Seohyun Park
- Department of Information Medicine, Asan Medical Center, 88, Olympicro 43gil, Songpagu, 05505, Seoul, Republic of Korea
| | - Kyuwoong Kim
- National Cancer Control Institute, National Cancer Center, 323 Ilsan-ro, Ilsandong-gu, 10408, Goyang, Republic of Korea
| | - Tae Joon Jun
- Big Data Research Center, Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43gil, Songpagu, 05505, Seoul, Republic of Korea
| | - Young-Hak Kim
- Division of Cardiology, Department of Information Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43gil, Songpagu, 05505, Seoul, Republic of Korea
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Huang A, Xie X, Yao X, Liu H, Wang X, Peng S. HF-DDI: Predicting Drug-Drug Interaction Events Based on Multimodal Hybrid Fusion. J Comput Biol 2023; 30:961-971. [PMID: 37594774 DOI: 10.1089/cmb.2023.0068] [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] [Indexed: 08/19/2023] Open
Abstract
Drug-drug interactions (DDIs) can have a significant impact on patient safety and health. Predicting potential DDIs before administering drugs to patients is a critical step in drug development and can help prevent adverse drug events. In this study, we propose a novel method called HF-DDI for predicting DDI events based on various drug features, including molecular structure, target, and enzyme information. Specifically, we design our model with both early fusion and late fusion strategies and utilize a score calculation module to predict the likelihood of interactions between drugs. Our model was trained and tested on a large data set of known DDIs, achieving an overall accuracy of 0.948. The results suggest that incorporating multiple drug features can improve the accuracy of DDI event prediction and may be useful for improving drug safety and patient outcomes.
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Affiliation(s)
- An Huang
- Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin, China
- College of Information Science and Engineering, Guilin University of Technology, Guilin, China
| | - Xiaolan Xie
- Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin, China
- College of Information Science and Engineering, Guilin University of Technology, Guilin, China
| | - Xiaojun Yao
- State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau, China
| | - Huanxiang Liu
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, China
| | - Xiaoqi Wang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Shaoliang Peng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
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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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Choi SR, Lee M. Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review. BIOLOGY 2023; 12:1033. [PMID: 37508462 PMCID: PMC10376273 DOI: 10.3390/biology12071033] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 07/18/2023] [Accepted: 07/21/2023] [Indexed: 07/30/2023]
Abstract
The emergence and rapid development of deep learning, specifically transformer-based architectures and attention mechanisms, have had transformative implications across several domains, including bioinformatics and genome data analysis. The analogous nature of genome sequences to language texts has enabled the application of techniques that have exhibited success in fields ranging from natural language processing to genomic data. This review provides a comprehensive analysis of the most recent advancements in the application of transformer architectures and attention mechanisms to genome and transcriptome data. The focus of this review is on the critical evaluation of these techniques, discussing their advantages and limitations in the context of genome data analysis. With the swift pace of development in deep learning methodologies, it becomes vital to continually assess and reflect on the current standing and future direction of the research. Therefore, this review aims to serve as a timely resource for both seasoned researchers and newcomers, offering a panoramic view of the recent advancements and elucidating the state-of-the-art applications in the field. Furthermore, this review paper serves to highlight potential areas of future investigation by critically evaluating studies from 2019 to 2023, thereby acting as a stepping-stone for further research endeavors.
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Affiliation(s)
- Sanghyuk Roy Choi
- School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
| | - Minhyeok Lee
- School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
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Hong E, Jeon J, Kim HU. Recent development of machine learning models for the prediction of drug-drug interactions. KOREAN J CHEM ENG 2023; 40:276-285. [PMID: 36748027 PMCID: PMC9894510 DOI: 10.1007/s11814-023-1377-3] [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: 09/11/2022] [Revised: 12/09/2022] [Accepted: 12/16/2022] [Indexed: 02/05/2023]
Abstract
Polypharmacy, the co-administration of multiple drugs, has become an area of concern as the elderly population grows and an unexpected infection, such as COVID-19 pandemic, keeps emerging. However, it is very costly and time-consuming to experimentally examine the pharmacological effects of polypharmacy. To address this challenge, machine learning models that predict drug-drug interactions (DDIs) have actively been developed in recent years. In particular, the growing volume of drug datasets and the advances in machine learning have facilitated the model development. In this regard, this review discusses the DDI-predicting machine learning models that have been developed since 2018. Our discussion focuses on dataset sources used to develop the models, featurization approaches of molecular structures and biological information, and types of DDI prediction outcomes from the models. Finally, we make suggestions for research opportunities in this field.
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Affiliation(s)
- Eujin Hong
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 Korea
| | - Junhyeok Jeon
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 Korea
| | - Hyun Uk Kim
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 Korea ,BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon, 34141 Korea
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