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Lu L, Qin J, Chen J, Yu N, Miyano S, Deng Z, Li C. Recent computational drug repositioning strategies against SARS-CoV-2. Comput Struct Biotechnol J 2022; 20:5713-5728. [PMID: 36277237 PMCID: PMC9575573 DOI: 10.1016/j.csbj.2022.10.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 10/12/2022] [Accepted: 10/12/2022] [Indexed: 11/08/2022] Open
Abstract
We performed a comprehensive review of computational drug repositioning methods applied to COVID-19 based on differing data types including sequence data, expression data, structure data and interaction data. We found that graph theory and neural network were the most used strategies for drug repositioning in the case of COVID-19. Integrating different levels of data may improve the success rate for drug repositioning.
Since COVID-19 emerged in 2019, significant levels of suffering and disruption have been caused on a global scale. Although vaccines have become widely used, the virus has shown its potential for evading immunities or acquiring other novel characteristics. Whether current drug treatments are still effective for people infected with Omicron remains unclear. Due to the long development cycles and high expense requirements of de novo drug development, many researchers have turned to consider drug repositioning in the search to find effective treatments for COVID-19. Here, we review such drug repositioning and combination efforts towards providing better handling. For potential drugs under consideration, aspects of both structure and function require attention, with specific categories of sequence, expression, structure, and interaction, the key parameters for investigation. For different data types, we show the corresponding differing drug repositioning methods that have been exploited. As incorporating drug combinations can increase therapeutic efficacy and reduce toxicity, we also review computational strategies to reveal drug combination potential. Taken together, we found that graph theory and neural network were the most used strategy with high potential towards drug repositioning for COVID-19. Integrating different levels of data may further improve the success rate of drug repositioning.
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Affiliation(s)
- Lu Lu
- Department of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China,Zhejiang Provincial Key Laboratory of Genetic & Developmental Disorders, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiale Qin
- Department of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China,Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Hangzhou, China
| | - Jiandong Chen
- Department of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China,School of Public Health, Undergraduate School of Zhejiang University, Hangzhou, China
| | - Na Yu
- Department of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Satoru Miyano
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Zhenzhong Deng
- Xinhua Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China,Corresponding authors at: Department of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China (C. Li).
| | - Chen Li
- Department of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China,Zhejiang Provincial Key Laboratory of Genetic & Developmental Disorders, Zhejiang University School of Medicine, Hangzhou, China,Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, China,Corresponding authors at: Department of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China (C. Li).
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Pomyen Y, Wanichthanarak K, Poungsombat P, Fahrmann J, Grapov D, Khoomrung S. Deep metabolome: Applications of deep learning in metabolomics. Comput Struct Biotechnol J 2020; 18:2818-2825. [PMID: 33133423 PMCID: PMC7575644 DOI: 10.1016/j.csbj.2020.09.033] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 09/21/2020] [Accepted: 09/21/2020] [Indexed: 01/11/2023] Open
Abstract
In the past few years, deep learning has been successfully applied to various omics data. However, the applications of deep learning in metabolomics are still relatively low compared to others omics. Currently, data pre-processing using convolutional neural network architecture appears to benefit the most from deep learning. Compound/structure identification and quantification using artificial neural network/deep learning performed relatively better than traditional machine learning techniques, whereas only marginally better results are observed in biological interpretations. Before deep learning can be effectively applied to metabolomics, several challenges should be addressed, including metabolome-specific deep learning architectures, dimensionality problems, and model evaluation regimes.
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Key Words
- AI, Artificial Intelligence
- ANN, Artificial Neural Network
- AUC, Area Under the receiver-operating characteristic Curve
- Artificial neural network
- CCS value, Collision Cross Section value
- CFM-EI, Competitive Fragmentation Modeling-Electron Ionization
- CNN, Convolutional Neural Network
- DL, Deep Learning
- DNN, Deep Neural Network
- Deep learning
- ECFP, Extended Circular Fingerprint
- ER, Estrogen Receptor
- FID, Free Induction Decay
- FP score, Fingerprint correlation score
- FTIR, Fourier Transform Infrared
- GC–MS, Gas Chromatography-Mass Spectrometry
- HDLSS data, High Dimensional Low Sample Size data
- IST, Iterative Soft Thresholding
- LC-MS, Liquid Chromatography-Mass Spectrometry
- LSTM, Long Short-Term Memory
- ML, Machine Learning
- MLP, Multi-layered Perceptron
- MS, Mass Spectrometry
- Mass spectrometry
- Metabolomics
- NEIMS, Neural Electron-Ionization Mass Spectrometry
- NMR
- NMR, Nuclear Magnetic Resonance
- NUS, Non-Uniformly Sampling
- PARAFAC2, Parallel Factor Analysis 2
- RF, Random Forest
- RNN, Recurrent Neural Network
- ReLU, Rectified Linear Unit
- SMARTS, SMILES arbitrary target specification
- SMILE, Sparse Multidimensional Iterative Lineshape-enhanced
- SMILES, Simplified Molecular-Input Line-Entry System
- SRA, Sequence Read Archive
- VAE, Variational Autoencoder
- istHMS, Implementation of IST at Harvard Medical School
- m/z, mass/charge ratio
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Affiliation(s)
- Yotsawat Pomyen
- Translational Research Unit, Chulabhorn Research Institute, Bangkok, Thailand
| | - Kwanjeera Wanichthanarak
- Metabolomics and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Patcha Poungsombat
- Metabolomics and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Center for Innovation in Chemistry (PERCH-CIC), Faculty of Science, Mahidol University, Rama 6 Road, Bangkok 10400, Thailand
| | - Johannes Fahrmann
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA
| | - Dmitry Grapov
- CDS- Creative Data Solutions LLC, https://creative-data.solutions, USA
| | - Sakda Khoomrung
- Metabolomics and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Center for Innovation in Chemistry (PERCH-CIC), Faculty of Science, Mahidol University, Rama 6 Road, Bangkok 10400, Thailand
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