101
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Chen Z, Liu X, Zhao P, Li C, Wang Y, Li F, Akutsu T, Bain C, Gasser RB, Li J, Yang Z, Gao X, Kurgan L, Song J. iFeatureOmega: an integrative platform for engineering, visualization and analysis of features from molecular sequences, structural and ligand data sets. Nucleic Acids Res 2022; 50:W434-W447. [PMID: 35524557 PMCID: PMC9252729 DOI: 10.1093/nar/gkac351] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/22/2022] [Accepted: 04/25/2022] [Indexed: 01/07/2023] Open
Abstract
The rapid accumulation of molecular data motivates development of innovative approaches to computationally characterize sequences, structures and functions of biological and chemical molecules in an efficient, accessible and accurate manner. Notwithstanding several computational tools that characterize protein or nucleic acids data, there are no one-stop computational toolkits that comprehensively characterize a wide range of biomolecules. We address this vital need by developing a holistic platform that generates features from sequence and structural data for a diverse collection of molecule types. Our freely available and easy-to-use iFeatureOmega platform generates, analyzes and visualizes 189 representations for biological sequences, structures and ligands. To the best of our knowledge, iFeatureOmega provides the largest scope when directly compared to the current solutions, in terms of the number of feature extraction and analysis approaches and coverage of different molecules. We release three versions of iFeatureOmega including a webserver, command line interface and graphical interface to satisfy needs of experienced bioinformaticians and less computer-savvy biologists and biochemists. With the assistance of iFeatureOmega, users can encode their molecular data into representations that facilitate construction of predictive models and analytical studies. We highlight benefits of iFeatureOmega based on three research applications, demonstrating how it can be used to accelerate and streamline research in bioinformatics, computational biology, and cheminformatics areas. The iFeatureOmega webserver is freely available at http://ifeatureomega.erc.monash.edu and the standalone versions can be downloaded from https://github.com/Superzchen/iFeatureOmega-GUI/ and https://github.com/Superzchen/iFeatureOmega-CLI/.
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Affiliation(s)
- Zhen Chen
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450046, China
- Center for Crop Genome Engineering, Henan Agricultural University, Zhengzhou 450046, China
| | - Xuhan Liu
- Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Einsteinweg 55, Leiden 2333 CC, The Netherlands
| | - Pei Zhao
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences (CAAS), Anyang 455000, China
| | - Chen Li
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
| | - Yanan Wang
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
| | - Fuyi Li
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto 611-0011, Japan
| | - Chris Bain
- Monash Data Future Institutes, Monash University, Melbourne, Victoria 3800, Australia
| | - Robin B Gasser
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Junzhou Li
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450046, China
| | - Zuoren Yang
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences (CAAS), Anyang 455000, China
| | - Xin Gao
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | - Jiangning Song
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
- Monash Data Future Institutes, Monash University, Melbourne, Victoria 3800, Australia
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102
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Gu F, Wu X, Wu W, Wang Z, Yang X, Chen Z, Wang Z, Chen G. Performance of deep learning in the detection of intracranial aneurysm: a systematic review and meta-analysis. Eur J Radiol 2022; 155:110457. [DOI: 10.1016/j.ejrad.2022.110457] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 06/17/2022] [Accepted: 07/25/2022] [Indexed: 12/12/2022]
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103
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Zhao Q, Yang M, Cheng Z, Li Y, Wang J. Biomedical Data and Deep Learning Computational Models for Predicting Compound-Protein Relations. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2092-2110. [PMID: 33769935 DOI: 10.1109/tcbb.2021.3069040] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The identification of compound-protein relations (CPRs), which includes compound-protein interactions (CPIs) and compound-protein affinities (CPAs), is critical to drug development. A common method for compound-protein relation identification is the use of in vitro screening experiments. However, the number of compounds and proteins is massive, and in vitro screening experiments are labor-intensive, expensive, and time-consuming with high failure rates. Researchers have developed a computational field called virtual screening (VS) to aid experimental drug development. These methods utilize experimentally validated biological interaction information to generate datasets and use the physicochemical and structural properties of compounds and target proteins as input information to train computational prediction models. At present, deep learning has been widely used in computer vision and natural language processing and has experienced epoch-making progress. At the same time, deep learning has also been used in the field of biomedicine widely, and the prediction of CPRs based on deep learning has developed rapidly and has achieved good results. The purpose of this study is to investigate and discuss the latest applications of deep learning techniques in CPR prediction. First, we describe the datasets and feature engineering (i.e., compound and protein representations and descriptors) commonly used in CPR prediction methods. Then, we review and classify recent deep learning approaches in CPR prediction. Next, a comprehensive comparison is performed to demonstrate the prediction performance of representative methods on classical datasets. Finally, we discuss the current state of the field, including the existing challenges and our proposed future directions. We believe that this investigation will provide sufficient references and insight for researchers to understand and develop new deep learning methods to enhance CPR predictions.
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104
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Bagherzadeh S, Shahabi MS, Shalbaf A. Detection of schizophrenia using hybrid of deep learning and brain effective connectivity image from electroencephalogram signal. Comput Biol Med 2022; 146:105570. [DOI: 10.1016/j.compbiomed.2022.105570] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/14/2022] [Accepted: 04/25/2022] [Indexed: 02/06/2023]
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105
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Rutherford C, Kafle P, Soos C, Epp T, Bradford L, Jenkins E. Investigating SARS-CoV-2 Susceptibility in Animal Species: A Scoping Review. ENVIRONMENTAL HEALTH INSIGHTS 2022; 16:11786302221107786. [PMID: 35782319 PMCID: PMC9247998 DOI: 10.1177/11786302221107786] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 05/30/2022] [Indexed: 06/15/2023]
Abstract
In the early stages of response to the SARS-CoV-2 pandemic, it was imperative for researchers to rapidly determine what animal species may be susceptible to the virus, under low knowledge and high uncertainty conditions. In this scoping review, the animal species being evaluated for SARS-CoV-2 susceptibility, the methods used to evaluate susceptibility, and comparing the evaluations between different studies were conducted. Using the PRISMA-ScR methodology, publications and reports from peer-reviewed and gray literature sources were collected from databases, Google Scholar, the World Organization for Animal Health (OIE), snowballing, and recommendations from experts. Inclusion and relevance criteria were applied, and information was subsequently extracted, categorized, summarized, and analyzed. Ninety seven sources (publications and reports) were identified which investigated 649 animal species from eight different classes: Mammalia, Aves, Actinopterygii, Reptilia, Amphibia, Insecta, Chondrichthyes, and Coelacanthimorpha. Sources used four different methods to evaluate susceptibility, in silico, in vitro, in vivo, and epidemiological analysis. Along with the different methods, how each source described "susceptibility" and evaluated the susceptibility of different animal species to SARS-CoV-2 varied, with conflicting susceptibility evaluations evident between different sources. Early in the pandemic, in silico methods were used the most to predict animal species susceptibility to SARS-CoV-2 and helped guide more costly and intensive studies using in vivo or epidemiological analyses. However, the limitations of all methods must be recognized, and evaluations made by in silico and in vitro should be re-evaluated when more information becomes available, such as demonstrated susceptibility through in vivo and epidemiological analysis.
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Affiliation(s)
- Connor Rutherford
- School of Public Health, University of
Saskatchewan, Saskatoon, SK, Canada
| | - Pratap Kafle
- Department of Veterinary Microbiology,
Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, SK,
Canada
- Department of Veterinary Biomedical
Sciences, Long Island University Post Campus, Brookville, NY, USA
| | - Catherine Soos
- Ecotoxicology and Wildlife Health
Division, Science & Technology Branch, Environment and Climate Change Canada,
Saskatoon, SK, Canada
- Department of Veterinary Pathology,
Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, SK,
Canada
| | - Tasha Epp
- Department of Large Animal Clinical
Sciences, Western College of Veterinary Medicine, University of Saskatchewan,
Saskatoon, SK, Canada
| | - Lori Bradford
- Ron and Jane Graham School of
Professional Development, College of Engineering, and School of Environment and
Sustainability, University of Saskatchewan, Saskatoon, SK, Canada
| | - Emily Jenkins
- Department of Veterinary Microbiology,
Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, SK,
Canada
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106
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Aziz M, Ejaz SA, Zargar S, Akhtar N, Aborode AT, A. Wani T, Batiha GES, Siddique F, Alqarni M, Akintola AA. Deep Learning and Structure-Based Virtual Screening for Drug Discovery against NEK7: A Novel Target for the Treatment of Cancer. Molecules 2022; 27:4098. [PMID: 35807344 PMCID: PMC9268522 DOI: 10.3390/molecules27134098] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 06/17/2022] [Accepted: 06/18/2022] [Indexed: 01/09/2023] Open
Abstract
NIMA-related kinase7 (NEK7) plays a multifunctional role in cell division and NLRP3 inflammasone activation. A typical expression or any mutation in the genetic makeup of NEK7 leads to the development of cancer malignancies and fatal inflammatory disease, i.e., breast cancer, non-small cell lung cancer, gout, rheumatoid arthritis, and liver cirrhosis. Therefore, NEK7 is a promising target for drug development against various cancer malignancies. The combination of drug repurposing and structure-based virtual screening of large libraries of compounds has dramatically improved the development of anticancer drugs. The current study focused on the virtual screening of 1200 benzene sulphonamide derivatives retrieved from the PubChem database by selecting and docking validation of the crystal structure of NEK7 protein (PDB ID: 2WQN). The compounds library was subjected to virtual screening using Auto Dock Vina. The binding energies of screened compounds were compared to standard Dabrafenib. In particular, compound 762 exhibited excellent binding energy of -42.67 kJ/mol, better than Dabrafenib (-33.89 kJ/mol). Selected drug candidates showed a reactive profile that was comparable to standard Dabrafenib. To characterize the stability of protein-ligand complexes, molecular dynamic simulations were performed, providing insight into the molecular interactions. The NEK7-Dabrafenib complex showed stability throughout the simulated trajectory. In addition, binding affinities, pIC50, and ADMET profiles of drug candidates were predicted using deep learning models. Deep learning models predicted the binding affinity of compound 762 best among all derivatives, which supports the findings of virtual screening. These findings suggest that top hits can serve as potential inhibitors of NEK7. Moreover, it is recommended to explore the inhibitory potential of identified hits compounds through in-vitro and in-vivo approaches.
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Affiliation(s)
- Mubashir Aziz
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan;
| | - Syeda Abida Ejaz
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan;
| | - Seema Zargar
- Department of Biochemistry, College of Science, King Saud University, P.O. Box 22452, Riyadh 11451, Saudi Arabia;
| | - Naveed Akhtar
- Department of Pharmaceutics, Faculty of Pharmacy, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan;
| | | | - Tanveer A. Wani
- Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh 11451, Saudi Arabia
| | - Gaber El-Saber Batiha
- Department of Pharmacology and Therapeutics, Faculty of Veterinary Medicine, Damanhour University, Damanhour 22511, AlBeheira, Egypt;
| | - Farhan Siddique
- Laboratory of Organic Electronics, Department of Science and Technology, Linköping University, SE-60174 Norrköping, Sweden;
- Department of Pharmacy, Royal Institute of Medical Sciences (RIMS), Multan 60000, Pakistan
| | - Mohammed Alqarni
- Department of Pharmaceutical Chemistry, College of Pharmacy, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia;
| | - Ashraf Akintayo Akintola
- Department of Biomedical Convergence Science and Technology, Kyungpook National University, Daegu 41566, Korea;
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107
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Monteiro NRC, Simões CJV, Ávila HV, Abbasi M, Oliveira JL, Arrais JP. Explainable deep drug-target representations for binding affinity prediction. BMC Bioinformatics 2022; 23:237. [PMID: 35715734 PMCID: PMC9204982 DOI: 10.1186/s12859-022-04767-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 05/25/2022] [Indexed: 11/10/2022] Open
Abstract
Background Several computational advances have been achieved in the drug discovery field, promoting the identification of novel drug–target interactions and new leads. However, most of these methodologies have been overlooking the importance of providing explanations to the decision-making process of deep learning architectures. In this research study, we explore the reliability of convolutional neural networks (CNNs) at identifying relevant regions for binding, specifically binding sites and motifs, and the significance of the deep representations extracted by providing explanations to the model’s decisions based on the identification of the input regions that contributed the most to the prediction. We make use of an end-to-end deep learning architecture to predict binding affinity, where CNNs are exploited in their capacity to automatically identify and extract discriminating deep representations from 1D sequential and structural data. Results The results demonstrate the effectiveness of the deep representations extracted from CNNs in the prediction of drug–target interactions. CNNs were found to identify and extract features from regions relevant for the interaction, where the weight associated with these spots was in the range of those with the highest positive influence given by the CNNs in the prediction. The end-to-end deep learning model achieved the highest performance both in the prediction of the binding affinity and on the ability to correctly distinguish the interaction strength rank order when compared to baseline approaches. Conclusions This research study validates the potential applicability of an end-to-end deep learning architecture in the context of drug discovery beyond the confined space of proteins and ligands with determined 3D structure. Furthermore, it shows the reliability of the deep representations extracted from the CNNs by providing explainability to the decision-making process. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04767-y.
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Affiliation(s)
- Nelson R C Monteiro
- Univ Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal.
| | | | - Henrique V Ávila
- Univ Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal
| | - Maryam Abbasi
- Univ Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal
| | - José L Oliveira
- IEETA, Department of Electronics, Telecommunications and Informatics, University of Aveiro, Aveiro, Portugal
| | - Joel P Arrais
- Univ Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal
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108
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Meli R, Morris GM, Biggin PC. Scoring Functions for Protein-Ligand Binding Affinity Prediction using Structure-Based Deep Learning: A Review. FRONTIERS IN BIOINFORMATICS 2022; 2:885983. [PMID: 36187180 PMCID: PMC7613667 DOI: 10.3389/fbinf.2022.885983] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 05/11/2022] [Indexed: 01/01/2023] Open
Abstract
The rapid and accurate in silico prediction of protein-ligand binding free energies or binding affinities has the potential to transform drug discovery. In recent years, there has been a rapid growth of interest in deep learning methods for the prediction of protein-ligand binding affinities based on the structural information of protein-ligand complexes. These structure-based scoring functions often obtain better results than classical scoring functions when applied within their applicability domain. Here we review structure-based scoring functions for binding affinity prediction based on deep learning, focussing on different types of architectures, featurization strategies, data sets, methods for training and evaluation, and the role of explainable artificial intelligence in building useful models for real drug-discovery applications.
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Affiliation(s)
- Rocco Meli
- Department of Biochemistry, University of Oxford, Oxford, United Kingdom
| | - Garrett M. Morris
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Philip C. Biggin
- Department of Biochemistry, University of Oxford, Oxford, United Kingdom
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109
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Zheng Y, Ma Y, Cammon J, Zhang S, Zhang J, Zhang Y. A new feature selection approach for driving fatigue EEG detection with a modified machine learning algorithm. Comput Biol Med 2022; 147:105718. [PMID: 35716435 DOI: 10.1016/j.compbiomed.2022.105718] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 05/19/2022] [Accepted: 06/06/2022] [Indexed: 11/16/2022]
Abstract
This study aims to identify new electroencephalography (EEG) features for the detection of driving fatigue. The most common EEG feature in driving fatigue detection is the power spectral density (PSD) of five frequency bands, i.e., alpha, beta, gamma, delta, and theta bands. PSD has proved to be useful, however its flaw is that it covers much implicit information of the time domain. In this study we propose a new approach, which combines ensemble empirical mode decomposition (EEMD) and PSD, to explore new EEG features for driving fatigue detection. Through EEMD we get a series of intrinsic mode function (IMF) components, from which we can extract PSD features. We used six features to compare with the proposed features, including the PSD of five frequency bands, PSD of empirical mode decomposition (EMD)-IMF components, PSD, permutation entropy (PE), sample entropy (SE), and fuzzy entropy (FE) of EEMD-IMF components, and common spatial pattern. Feature overlap ratio and multiple machine learning methods were applied to evaluate these feature extraction approaches. The results show that the classification accuracy and overlap ratio of experiments based on IMF's energy spectrum is far superior to other features. Through channel optimization and a comparison of accuracy, we conclude that our new feature selection approach has a better performance based on the modified hierarchical extreme learning machine algorithm with Particle Swarm Optimization (PSO-H-ELM) classifier, which has the highest average accuracy of 97.53%.
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Affiliation(s)
- Yun Zheng
- Intelligent Control & Robotics Institute, College of Automation, Hangzhou Dianzi University, Hangzhou, China.
| | - Yuliang Ma
- Intelligent Control & Robotics Institute, College of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Jared Cammon
- Department of Biomedical Engineering, University of Houston, Houston, TX, USA
| | - Songjie Zhang
- College of Electrical Engineering, Zhejiang University, Hangzhou, China
| | - Jianhai Zhang
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China
| | - Yingchun Zhang
- Department of Biomedical Engineering, University of Houston, Houston, TX, USA.
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110
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Shin SH, Oh SM, Yoon Park JH, Lee KW, Yang H. OptNCMiner: a deep learning approach for the discovery of natural compounds modulating disease-specific multi-targets. BMC Bioinformatics 2022; 23:218. [PMID: 35672685 PMCID: PMC9175487 DOI: 10.1186/s12859-022-04752-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 05/25/2022] [Indexed: 11/22/2022] Open
Abstract
Background Due to their diverse bioactivity, natural product (NP)s have been developed as commercial products in the pharmaceutical, food and cosmetic sectors as natural compound (NC)s and in the form of extracts. Following administration, NCs typically interact with multiple target proteins to elicit their effects. Various machine learning models have been developed to predict multi-target modulating NCs with desired physiological effects. However, due to deficiencies with existing chemical-protein interaction datasets, which are mostly single-labeled and limited, the existing models struggle to predict new chemical-protein interactions. New techniques are needed to overcome these limitations. Results We propose a novel NC discovery model called OptNCMiner that offers various advantages. The model is trained via end-to-end learning with a feature extraction step implemented, and it predicts multi-target modulating NCs through multi-label learning. In addition, it offers a few-shot learning approach to predict NC-protein interactions using a small training dataset. OptNCMiner achieved better prediction performance in terms of recall than conventional classification models. It was tested for the prediction of NC-protein interactions using small datasets and for a use case scenario to identify multi-target modulating NCs for type 2 diabetes mellitus complications. Conclusions OptNCMiner identifies NCs that modulate multiple target proteins, which facilitates the discovery and the understanding of biological activity of novel NCs with desirable health benefits.
Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04752-5.
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Affiliation(s)
- Seo Hyun Shin
- Department of Agricultural Biotechnology, Seoul National University, Seoul, 08826, Republic of Korea
| | - Seung Man Oh
- Department of Agricultural Biotechnology, Seoul National University, Seoul, 08826, Republic of Korea
| | - Jung Han Yoon Park
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea
| | - Ki Won Lee
- Department of Agricultural Biotechnology, Seoul National University, Seoul, 08826, Republic of Korea. .,Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea. .,Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Hee Yang
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
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111
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Yurina V, Adianingsih OR. Predicting epitopes for vaccine development using bioinformatics tools. Ther Adv Vaccines Immunother 2022; 10:25151355221100218. [PMID: 35647486 PMCID: PMC9130818 DOI: 10.1177/25151355221100218] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 04/14/2022] [Indexed: 11/20/2022] Open
Abstract
Epitope-based DNA vaccine development is one application of bioinformatics or
in silico studies, that is, computational methods,
including mathematical, chemical, and biological approaches, which are widely
used in drug development. Many in silico studies have been
conducted to analyze the efficacy, safety, toxicity effects, and interactions of
drugs. In the vaccine design process, in silico studies are
performed to predict epitopes that could trigger T-cell and B-cell reactions
that would produce both cellular and humoral immune responses. Immunoinformatics
is the branch of bioinformatics used to study the relationship between immune
responses and predicted epitopes. Progress in immunoinformatics has been rapid
and has led to the development of a variety of tools that are used for the
prediction of epitopes recognized by B cells or T cells as well as the antigenic
responses. However, the in silico approach to vaccine design is
still relatively new; thus, this review is aimed at increasing understanding of
the importance of in silico studies in the design of vaccines
and thereby facilitating future research in this field.
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Affiliation(s)
- Valentina Yurina
- Department of Pharmacy, Medical Faculty, Universitas Brawijaya, Jalan Veteran, Malang 65145, East Java, Indonesia
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112
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Kothandan S, Radhakrishana A, Kuppusamy G. Review on Artificial Intelligence Based Ophthalmic Application. Curr Pharm Des 2022; 28:2150-2160. [PMID: 35619317 DOI: 10.2174/1381612828666220520112240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 02/14/2022] [Indexed: 11/22/2022]
Abstract
Artificial intelligence is the leading branch of technology and innovation. The utility of artificial intelligence in the field of medicine is also remarkable. From drug discovery and development till the introduction of products in the market, artificial intelligence can play its role. As people age, they are more prone to be affected by eye diseases around the globe. Early diagnosis and detection help in minimizing the risk of vision loss and providing a quality life. With the help of artificial intelligence, the workload of humans and manmade errors can be reduced to an extent. The need for artificial intelligence in the area of ophthalmic is also found to be significant. As people age, they are more prone to be affected by eye diseases around the globe. Early diagnosis and detection help in minimizing the risk of vision loss and providing a quality life. In this review, we elaborated on the use of artificial intelligence in the field of pharmaceutical product development mainly with its application in ophthalmic care. AI in the future has a high potential to increase the success rate in the drug discovery phase has already been established. The application of artificial intelligence for drug development, diagnosis, and treatment is also reported with the scientific evidence in this paper.
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Affiliation(s)
- Sudhakar Kothandan
- Department of Pharmaceutics, JSS College of Pharmacy (JSS Academy of Higher Education & Research), Ooty
| | - Arun Radhakrishana
- Department of Pharmaceutics, JSS College of Pharmacy (JSS Academy of Higher Education & Research), Ooty
| | - Gowthamarajan Kuppusamy
- Department of Pharmaceutics, JSS College of Pharmacy (JSS Academy of Higher Education & Research), Ooty
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113
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Wei Y, Li S, Li Z, Wan Z, Lin J. Interpretable-ADMET: a web service for ADMET prediction and optimization based on deep neural representation. Bioinformatics 2022; 38:2863-2871. [PMID: 35561160 DOI: 10.1093/bioinformatics/btac192] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 03/05/2022] [Accepted: 03/28/2022] [Indexed: 11/15/2022] Open
Abstract
MOTIVATION In the process of discovery and optimization of lead compounds, it is difficult for non-expert pharmacologists to intuitively determine the contribution of substructure to a particular property of a molecule. RESULTS In this work, we develop a user-friendly web service, named interpretable-absorption, distribution, metabolism, excretion and toxicity (ADMET), which predict 59 ADMET-associated properties using 90 qualitative classification models and 28 quantitative regression models based on graph convolutional neural network and graph attention network algorithms. In interpretable-ADMET, there are 250 729 entries associated with 59 kinds of ADMET-associated properties for 80 167 chemical compounds. In addition to making predictions, interpretable-ADMET provides interpretation models based on gradient-weighted class activation map for identifying the substructure, which is important to the particular property. Interpretable-ADMET also provides an optimize module to automatically generate a set of novel virtual candidates based on matched molecular pair rules. We believe that interpretable-ADMET could serve as a useful tool for lead optimization in drug discovery. AVAILABILITY AND IMPLEMENTATION Interpretable-ADMET is available at http://cadd.pharmacy.nankai.edu.cn/interpretableadmet/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yu Wei
- State Key Laboratory of Medicinal Chemical Biology, Frontiers Science Center for Cell Responses, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300353, China
| | - Shanshan Li
- State Key Laboratory of Medicinal Chemical Biology, Frontiers Science Center for Cell Responses, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300353, China
- Platform of Pharmaceutical Intelligence, Tianjin International Joint Academy of Biomedicine, Tianjin 300457, China
| | - Zhonglin Li
- State Key Laboratory of Medicinal Chemical Biology, Frontiers Science Center for Cell Responses, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300353, China
- Platform of Pharmaceutical Intelligence, Tianjin International Joint Academy of Biomedicine, Tianjin 300457, China
| | - Ziwei Wan
- State Key Laboratory of Medicinal Chemical Biology, Frontiers Science Center for Cell Responses, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300353, China
- Platform of Pharmaceutical Intelligence, Tianjin International Joint Academy of Biomedicine, Tianjin 300457, China
| | - Jianping Lin
- State Key Laboratory of Medicinal Chemical Biology, Frontiers Science Center for Cell Responses, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300353, China
- Platform of Pharmaceutical Intelligence, Tianjin International Joint Academy of Biomedicine, Tianjin 300457, China
- Biodesign Center, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
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114
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Identification of Potential WSB1 Inhibitors by AlphaFold Modeling, Virtual Screening, and Molecular Dynamics Simulation Studies. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2022; 2022:4629392. [PMID: 35600960 PMCID: PMC9122669 DOI: 10.1155/2022/4629392] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 04/27/2022] [Indexed: 12/03/2022]
Abstract
WD40 repeat and SOCS box containing 1 (WSB1) consists of seven WD40 repeat structural domains at the N-terminal end and one SOCS box structural domain at the C-terminal end. WSB1 promotes cancer progression by affecting the Von Hippel–Lindau tumor suppressor protein (pVHL) and upregulating hypoxia inducible factor-1α (HIF-1α) target gene expression. However, the crystal structure of WSB1 has not been reported, which is not beneficial to the research on WSB1 inhibitors. Therefore, we focused on specific small molecule inhibitors of WSB1. This study applied virtual screening and molecular dynamics simulations; finally, 20 compounds were obtained. Among them, compound G490-0341 showed the best stable structure and was a promising composite for further development of WSB1 inhibitors.
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115
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Tran HNT, Thomas JJ, Ahamed Hassain Malim NH. DeepNC: a framework for drug-target interaction prediction with graph neural networks. PeerJ 2022; 10:e13163. [PMID: 35578674 PMCID: PMC9107302 DOI: 10.7717/peerj.13163] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 03/03/2022] [Indexed: 01/12/2023] Open
Abstract
The exploration of drug-target interactions (DTI) is an essential stage in the drug development pipeline. Thanks to the assistance of computational models, notably in the deep learning approach, scientists have been able to shorten the time spent on this stage. Widely practiced deep learning algorithms such as convolutional neural networks and recurrent neural networks are commonly employed in DTI prediction projects. However, they can hardly utilize the natural graph structure of molecular inputs. For that reason, a graph neural network (GNN) is an applicable choice for learning the chemical and structural characteristics of molecules when it represents molecular compounds as graphs and learns the compound features from those graphs. In an effort to construct an advanced deep learning-based model for DTI prediction, we propose Deep Neural Computation (DeepNC), which is a framework utilizing three GNN algorithms: Generalized Aggregation Networks (GENConv), Graph Convolutional Networks (GCNConv), and Hypergraph Convolution-Hypergraph Attention (HypergraphConv). In short, our framework learns the features of drugs and targets by the layers of GNN and 1-D convolution network, respectively. Then, representations of the drugs and targets are fed into fully-connected layers to predict the binding affinity values. The models of DeepNC were evaluated on two benchmarked datasets (Davis, Kiba) and one independently proposed dataset (Allergy) to confirm that they are suitable for predicting the binding affinity of drugs and targets. Moreover, compared to the results of baseline methods that worked on the same problem, DeepNC proves to improve the performance in terms of mean square error and concordance index.
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Affiliation(s)
- Huu Ngoc Tran Tran
- Department of Computing, UOW Malaysia, KDU Penang University College, George Town, Penang, Malaysia
| | - J. Joshua Thomas
- Department of Computing, UOW Malaysia, KDU Penang University College, George Town, Penang, Malaysia
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116
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DRUG REPOSITIONING FOR CANCER IN THE ERA OF BIG OMICS AND REAL-WORLD DATA. Crit Rev Oncol Hematol 2022; 175:103730. [DOI: 10.1016/j.critrevonc.2022.103730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 05/25/2022] [Accepted: 05/27/2022] [Indexed: 11/15/2022] Open
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117
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Hu RS, Hesham AEL, Zou Q. Machine Learning and Its Applications for Protozoal Pathogens and Protozoal Infectious Diseases. Front Cell Infect Microbiol 2022; 12:882995. [PMID: 35573796 PMCID: PMC9097758 DOI: 10.3389/fcimb.2022.882995] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 03/28/2022] [Indexed: 12/24/2022] Open
Abstract
In recent years, massive attention has been attracted to the development and application of machine learning (ML) in the field of infectious diseases, not only serving as a catalyst for academic studies but also as a key means of detecting pathogenic microorganisms, implementing public health surveillance, exploring host-pathogen interactions, discovering drug and vaccine candidates, and so forth. These applications also include the management of infectious diseases caused by protozoal pathogens, such as Plasmodium, Trypanosoma, Toxoplasma, Cryptosporidium, and Giardia, a class of fatal or life-threatening causative agents capable of infecting humans and a wide range of animals. With the reduction of computational cost, availability of effective ML algorithms, popularization of ML tools, and accumulation of high-throughput data, it is possible to implement the integration of ML applications into increasing scientific research related to protozoal infection. Here, we will present a brief overview of important concepts in ML serving as background knowledge, with a focus on basic workflows, popular algorithms (e.g., support vector machine, random forest, and neural networks), feature extraction and selection, and model evaluation metrics. We will then review current ML applications and major advances concerning protozoal pathogens and protozoal infectious diseases through combination with correlative biology expertise and provide forward-looking insights for perspectives and opportunities in future advances in ML techniques in this field.
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Affiliation(s)
- Rui-Si Hu
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Abd El-Latif Hesham
- Genetics Department, Faculty of Agriculture, Beni-Suef University, Beni-Suef, Egypt
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
- *Correspondence: Quan Zou,
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118
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Cheng C, Liu M, Gao X, Wu D, Pu M, Ma J, Quinn RJ, Xiao Z, Liu Z. Identifying New Ligands for JNK3 by Fluorescence Thermal Shift Assays and Native Mass Spectrometry. ACS OMEGA 2022; 7:13925-13931. [PMID: 35559183 PMCID: PMC9088906 DOI: 10.1021/acsomega.2c00340] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 04/05/2022] [Indexed: 06/15/2023]
Abstract
The c-Jun N-terminal kinases (JNKs) are evolutionary highly conserved serine/threonine kinases. Numerous findings suggest that JNK3 is involved in the pathogenesis of neurodegenerative diseases, so the inhibition of JNK3 may be a potential therapeutic intervention. The identification of novel compounds with promising pharmacological properties still represents a challenge. Fluorescence thermal shift screening of a chemically diversified lead-like scaffold library of 2024 pure compounds led to the initial identification of seven JNK3 binding hits, which were classified into four scaffold groups according to their chemical structures. Native mass spectrometry validated the interaction of 4 out of the 7 hits with JNK3. Binding geometries and interactions of the top 2 hits were evaluated by docking into a JNK3 crystal structure. Hit 5 had a K d of 21 μM with JNK3 suggested scaffold 5-(phenylamino)-1H-1,2,3-triazole-4-carboxamide as a novel and selective JNK3 binder.
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Affiliation(s)
- Chongyun Cheng
- National
Laboratory of Biomacromolecules, Institute
of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- Griffith
Institute for Drug Discovery, Griffith University, Brisbane, Queensland 4111, Australia
- Monash
Biomedicine Discovery Institute, Monash
University, Melbourne, Victoria 3800, Australia
| | - Miaomiao Liu
- Griffith
Institute for Drug Discovery, Griffith University, Brisbane, Queensland 4111, Australia
| | - Xiaoqin Gao
- State
Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical
Sciences, Peking University, Beijing 100191, China
| | - Dong Wu
- iHuman
Institute, ShanghaiTech University, Shanghai 201210, China
| | - Mengchen Pu
- National
Laboratory of Biomacromolecules, Institute
of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Jun Ma
- Griffith
Institute for Drug Discovery, Griffith University, Brisbane, Queensland 4111, Australia
| | - Ronald J. Quinn
- Griffith
Institute for Drug Discovery, Griffith University, Brisbane, Queensland 4111, Australia
| | - Zhicheng Xiao
- Monash
Biomedicine Discovery Institute, Monash
University, Melbourne, Victoria 3800, Australia
- Kunming
Medical College, Kunming, Yunnan 650031, China
| | - Zhijie Liu
- National
Laboratory of Biomacromolecules, Institute
of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- iHuman
Institute, ShanghaiTech University, Shanghai 201210, China
- Kunming
Medical College, Kunming, Yunnan 650031, China
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119
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Periwal V, Bassler S, Andrejev S, Gabrielli N, Patil KR, Typas A, Patil KR. Bioactivity assessment of natural compounds using machine learning models trained on target similarity between drugs. PLoS Comput Biol 2022; 18:e1010029. [PMID: 35468126 PMCID: PMC9071136 DOI: 10.1371/journal.pcbi.1010029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 05/05/2022] [Accepted: 03/17/2022] [Indexed: 11/19/2022] Open
Abstract
Natural compounds constitute a rich resource of potential small molecule therapeutics. While experimental access to this resource is limited due to its vast diversity and difficulties in systematic purification, computational assessment of structural similarity with known therapeutic molecules offers a scalable approach. Here, we assessed functional similarity between natural compounds and approved drugs by combining multiple chemical similarity metrics and physicochemical properties using a machine-learning approach. We computed pairwise similarities between 1410 drugs for training classification models and used the drugs shared protein targets as class labels. The best performing models were random forest which gave an average area under the ROC of 0.9, Matthews correlation coefficient of 0.35, and F1 score of 0.33, suggesting that it captured the structure-activity relation well. The models were then used to predict protein targets of circa 11k natural compounds by comparing them with the drugs. This revealed therapeutic potential of several natural compounds, including those with support from previously published sources as well as those hitherto unexplored. We experimentally validated one of the predicted pair’s activities, viz., Cox-1 inhibition by 5-methoxysalicylic acid, a molecule commonly found in tea, herbs and spices. In contrast, another natural compound, 4-isopropylbenzoic acid, with the highest similarity score when considering most weighted similarity metric but not picked by our models, did not inhibit Cox-1. Our results demonstrate the utility of a machine-learning approach combining multiple chemical features for uncovering protein binding potential of natural compounds.
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Affiliation(s)
- Vinita Periwal
- European Molecular Biology Laboratory, Heidelberg, Germany
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Stefan Bassler
- European Molecular Biology Laboratory, Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | | | | | - Kaustubh Raosaheb Patil
- Institute of Neuroscience and Medicine (INM-7), Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | | | - Kiran Raosaheb Patil
- European Molecular Biology Laboratory, Heidelberg, Germany
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, United Kingdom
- * E-mail:
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120
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Gomes IDS, Santana CA, Marcolino LS, de Lima LHF, de Melo-Minardi RC, Dias RS, de Paula SO, Silveira SDA. Computational prediction of potential inhibitors for SARS-COV-2 main protease based on machine learning, docking, MM-PBSA calculations, and metadynamics. PLoS One 2022; 17:e0267471. [PMID: 35452494 PMCID: PMC9032443 DOI: 10.1371/journal.pone.0267471] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 04/06/2022] [Indexed: 11/23/2022] Open
Abstract
The development of new drugs is a very complex and time-consuming process, and for this reason, researchers have been resorting heavily to drug repurposing techniques as an alternative for the treatment of various diseases. This approach is especially interesting when it comes to emerging diseases with high rates of infection, because the lack of a quickly cure brings many human losses until the mitigation of the epidemic, as is the case of COVID-19. In this work, we combine an in-house developed machine learning strategy with docking, MM-PBSA calculations, and metadynamics to detect potential inhibitors for SARS-COV-2 main protease among FDA approved compounds. To assess the ability of our machine learning strategy to retrieve potential compounds we calculated the Enrichment Factor of compound datasets for three well known protein targets: HIV-1 reverse transcriptase (PDB 4B3P), 5-HT2A serotonin receptor (PDB 6A94), and H1 histamine receptor (PDB 3RZE). The Enrichment Factor for each target was, respectively, 102.5, 12.4, 10.6, which are considered significant values. Regarding the identification of molecules that can potentially inhibit the main protease of SARS-COV-2, compounds output by the machine learning step went through a docking experiment against SARS-COV-2 Mpro. The best scored poses were the input for MM-PBSA calculations and metadynamics using CHARMM and AMBER force fields to predict the binding energy for each complex. Our work points out six molecules, highlighting the strong interaction obtained for Mpro-mirabegron complex. Among these six, to the best of our knowledge, ambenonium has not yet been described in the literature as a candidate inhibitor for the SARS-COV-2 main protease in its active pocket.
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Affiliation(s)
- Isabela de Souza Gomes
- Department of Computer Science, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil
| | - Charles Abreu Santana
- Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
- Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | | | - Leonardo Henrique França de Lima
- Department of Exact and Biological Sciences, Universidade Federal de São João del-Rei, Sete Lagoas Campus, Sete Lagoas, Minas Gerais, Brazil
| | - Raquel Cardoso de Melo-Minardi
- Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
- Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Roberto Sousa Dias
- Department of General Biology, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil
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121
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Yu L, Zhang Y, Xue L, Liu F, Chen Q, Luo J, Jing R. Systematic Analysis and Accurate Identification of DNA N4-Methylcytosine Sites by Deep Learning. Front Microbiol 2022; 13:843425. [PMID: 35401453 PMCID: PMC8989013 DOI: 10.3389/fmicb.2022.843425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Accepted: 02/21/2022] [Indexed: 11/13/2022] Open
Abstract
DNA N4-methylcytosine (4mC) is a pivotal epigenetic modification that plays an essential role in DNA replication, repair, expression and differentiation. To gain insight into the biological functions of 4mC, it is critical to identify their modification sites in the genomics. Recently, deep learning has become increasingly popular in recent years and frequently employed for the 4mC site identification. However, a systematic analysis of how to build predictive models using deep learning techniques is still lacking. In this work, we first summarized all existing deep learning-based predictors and systematically analyzed their models, features and datasets, etc. Then, using a typical standard dataset with three species (A. thaliana, C. elegans, and D. melanogaster), we assessed the contribution of different model architectures, encoding methods and the attention mechanism in establishing a deep learning-based model for the 4mC site prediction. After a series of optimizations, convolutional-recurrent neural network architecture using the one-hot encoding and attention mechanism achieved the best overall prediction performance. Extensive comparison experiments were conducted based on the same dataset. This work will be helpful for researchers who would like to build the 4mC prediction models using deep learning in the future.
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Affiliation(s)
- Lezheng Yu
- School of Chemistry and Materials Science, Guizhou Education University, Guiyang, China
| | - Yonglin Zhang
- Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou, China
| | - Li Xue
- School of Public Health, Southwest Medical University, Luzhou, China
| | - Fengjuan Liu
- School of Geography and Resources, Guizhou Education University, Guiyang, China
| | - Qi Chen
- Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Jiesi Luo
- Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou, China.,Department of Pharmacy, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Runyu Jing
- School of Cyber Science and Engineering, Sichuan University, Chengdu, China
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122
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Promoting Sustainability through Next-Generation Biologics Drug Development. SUSTAINABILITY 2022. [DOI: 10.3390/su14084401] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
The fourth industrial revolution in 2011 aimed to transform the traditional manufacturing processes. As part of this revolution, disruptive innovations in drug development and data science approaches have the potential to optimize CMC (chemistry, manufacture, and control). The real-time simulation of processes using “digital twins” can maximize efficiency while improving sustainability. As part of this review, we investigate how the World Health Organization’s 17 sustainability goals can apply toward next-generation drug development. We analyze the state-of-the-art laboratory leadership, inclusive personnel recruiting, the latest therapy approaches, and intelligent process automation. We also outline how modern data science techniques and machine tools for CMC help to shorten drug development time, reduce failure rates, and minimize resource usage. Finally, we systematically analyze and compare existing approaches to our experiences with the high-throughput laboratory KIWI-biolab at the TU Berlin. We describe a sustainable business model that accelerates scientific innovations and supports global action toward a sustainable future.
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123
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Application of explainable artificial intelligence in the identification of Squamous Cell Carcinoma biomarkers. Comput Biol Med 2022; 146:105505. [DOI: 10.1016/j.compbiomed.2022.105505] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Revised: 04/03/2022] [Accepted: 04/05/2022] [Indexed: 11/23/2022]
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124
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Karasev DA, Sobolev BN, Lagunin AA, Filimonov DA, Poroikov VV. The method predicting interaction between protein targets and small-molecular ligands with the wide applicability domain. Comput Biol Chem 2022; 98:107674. [DOI: 10.1016/j.compbiolchem.2022.107674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 03/24/2022] [Accepted: 03/28/2022] [Indexed: 11/03/2022]
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125
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Zhang J, Wang Q, Shen W. Hyper-parameter optimization of multiple machine learning algorithms for molecular property prediction using hyperopt library. Chin J Chem Eng 2022. [DOI: 10.1016/j.cjche.2022.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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126
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Vishwakarma P, Vattekatte AM, Shinada N, Diharce J, Martins C, Cadet F, Gardebien F, Etchebest C, Nadaradjane AA, de Brevern AG. V HH Structural Modelling Approaches: A Critical Review. Int J Mol Sci 2022; 23:3721. [PMID: 35409081 PMCID: PMC8998791 DOI: 10.3390/ijms23073721] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 03/23/2022] [Accepted: 03/23/2022] [Indexed: 12/20/2022] Open
Abstract
VHH, i.e., VH domains of camelid single-chain antibodies, are very promising therapeutic agents due to their significant physicochemical advantages compared to classical mammalian antibodies. The number of experimentally solved VHH structures has significantly improved recently, which is of great help, because it offers the ability to directly work on 3D structures to humanise or improve them. Unfortunately, most VHHs do not have 3D structures. Thus, it is essential to find alternative ways to get structural information. The methods of structure prediction from the primary amino acid sequence appear essential to bypass this limitation. This review presents the most extensive overview of structure prediction methods applied for the 3D modelling of a given VHH sequence (a total of 21). Besides the historical overview, it aims at showing how model software programs have been shaping the structural predictions of VHHs. A brief explanation of each methodology is supplied, and pertinent examples of their usage are provided. Finally, we present a structure prediction case study of a recently solved VHH structure. According to some recent studies and the present analysis, AlphaFold 2 and NanoNet appear to be the best tools to predict a structural model of VHH from its sequence.
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Affiliation(s)
- Poonam Vishwakarma
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-75015 Paris, France; (P.V.); (A.M.V.); (J.D.); (C.M.); (C.E.); (A.A.N.)
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-97715 Saint Denis Messag, France; (F.C.); (F.G.)
| | - Akhila Melarkode Vattekatte
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-75015 Paris, France; (P.V.); (A.M.V.); (J.D.); (C.M.); (C.E.); (A.A.N.)
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-97715 Saint Denis Messag, France; (F.C.); (F.G.)
| | | | - Julien Diharce
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-75015 Paris, France; (P.V.); (A.M.V.); (J.D.); (C.M.); (C.E.); (A.A.N.)
| | - Carla Martins
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-75015 Paris, France; (P.V.); (A.M.V.); (J.D.); (C.M.); (C.E.); (A.A.N.)
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-97715 Saint Denis Messag, France; (F.C.); (F.G.)
| | - Frédéric Cadet
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-97715 Saint Denis Messag, France; (F.C.); (F.G.)
- PEACCEL, Artificial Intelligence Department, Square Albin Cachot, F-75013 Paris, France
| | - Fabrice Gardebien
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-97715 Saint Denis Messag, France; (F.C.); (F.G.)
| | - Catherine Etchebest
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-75015 Paris, France; (P.V.); (A.M.V.); (J.D.); (C.M.); (C.E.); (A.A.N.)
| | - Aravindan Arun Nadaradjane
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-75015 Paris, France; (P.V.); (A.M.V.); (J.D.); (C.M.); (C.E.); (A.A.N.)
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-97715 Saint Denis Messag, France; (F.C.); (F.G.)
| | - Alexandre G. de Brevern
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-75015 Paris, France; (P.V.); (A.M.V.); (J.D.); (C.M.); (C.E.); (A.A.N.)
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-97715 Saint Denis Messag, France; (F.C.); (F.G.)
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127
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Cong X, Ren W, Pacalon J, Xu R, Xu L, Li X, de March CA, Matsunami H, Yu H, Yu Y, Golebiowski J. Large-Scale G Protein-Coupled Olfactory Receptor-Ligand Pairing. ACS CENTRAL SCIENCE 2022; 8:379-387. [PMID: 35350604 PMCID: PMC8949627 DOI: 10.1021/acscentsci.1c01495] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Indexed: 05/22/2023]
Abstract
G protein-coupled receptors (GPCRs) conserve common structural folds and activation mechanisms, yet their ligand spectra and functions are highly diverse. This work investigated how the amino-acid sequences of olfactory receptors (ORs)-the largest GPCR family-encode diversified responses to various ligands. We established a proteochemometric (PCM) model based on OR sequence similarities and ligand physicochemical features to predict OR responses to odorants using supervised machine learning. The PCM model was constructed with the aid of site-directed mutagenesis, in vitro functional assays, and molecular simulations. We found that the ligand selectivity of the ORs is mostly encoded in the residues up to 8 Å around the orthosteric pocket. Subsequent predictions using Random Forest (RF) showed a hit rate of up to 58%, as assessed by in vitro functional assays of 111 ORs and 7 odorants of distinct scaffolds. Sixty-four new OR-odorant pairs were discovered, and 25 ORs were deorphanized here. The best model demonstrated a 56% deorphanization rate. The PCM-RF approach will accelerate OR-odorant mapping and OR deorphanization.
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Affiliation(s)
- Xiaojing Cong
- Université
Côte d’Azur, CNRS, Institut de Chimie de Nice UMR7272, Nice 06108, France
- E-mail:
| | - Wenwen Ren
- Institutes
of Biomedical Sciences, Fudan University, Shanghai 200031, People’s Republic of China
| | - Jody Pacalon
- Université
Côte d’Azur, CNRS, Institut de Chimie de Nice UMR7272, Nice 06108, France
| | - Rui Xu
- School
of Life Sciences, Shanghai University, Shanghai 200444, People’s Republic of China
| | - Lun Xu
- Ear,
Nose & Throat Institute, Department of Otolaryngology, Eye, Ear,
Nose & Throat Hospital, Fudan University, Shanghai 200031, People’s Republic of China
| | - Xuewen Li
- School
of Life Sciences, Shanghai University, Shanghai 200444, People’s Republic of China
| | - Claire A. de March
- Department
of Molecular Genetics and Microbiology, and Department of Neurobiology,
and Duke Institute for Brain Sciences, Duke
University Medical Center, Research Drive, Durham, North Carolina 27710, United States
| | - Hiroaki Matsunami
- Department
of Molecular Genetics and Microbiology, and Department of Neurobiology,
and Duke Institute for Brain Sciences, Duke
University Medical Center, Research Drive, Durham, North Carolina 27710, United States
| | - Hongmeng Yu
- Ear,
Nose & Throat Institute, Department of Otolaryngology, Eye, Ear,
Nose & Throat Hospital, Fudan University, Shanghai 200031, People’s Republic of China
- Clinical
and Research Center for Olfactory Disorders, Eye, Ear, Nose &
Throat Hospital, Fudan University, Shanghai 200031, People’s Republic of China
- Research
Units of New Technologies of Endoscopic Surgery in Skull Base Tumor,
Chinese Academy of Medical Sciences, Beijing 100730, People’s
Republic of China
| | - Yiqun Yu
- Ear,
Nose & Throat Institute, Department of Otolaryngology, Eye, Ear,
Nose & Throat Hospital, Fudan University, Shanghai 200031, People’s Republic of China
- Clinical
and Research Center for Olfactory Disorders, Eye, Ear, Nose &
Throat Hospital, Fudan University, Shanghai 200031, People’s Republic of China
- E-mail:
| | - Jérôme Golebiowski
- Université
Côte d’Azur, CNRS, Institut de Chimie de Nice UMR7272, Nice 06108, France
- Department
of Brain and Cognitive Sciences, Daegu Gyeongbuk
Institute of Science and Technology, Daegu 711-873, South Korea
- E-mail:
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Du BX, Qin Y, Jiang YF, Xu Y, Yiu SM, Yu H, Shi JY. Compound–protein interaction prediction by deep learning: Databases, descriptors and models. Drug Discov Today 2022; 27:1350-1366. [DOI: 10.1016/j.drudis.2022.02.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 11/19/2021] [Accepted: 02/28/2022] [Indexed: 11/24/2022]
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Altalib MK, Salim N. Similarity-Based Virtual Screen Using Enhanced Siamese Deep Learning Methods. ACS OMEGA 2022; 7:4769-4786. [PMID: 35187297 PMCID: PMC8851658 DOI: 10.1021/acsomega.1c04587] [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: 08/23/2021] [Accepted: 01/17/2022] [Indexed: 06/14/2023]
Abstract
Traditional drug production is a long and complex process that leads to new drug production. The virtual screening technique is a computational method that allows chemical compounds to be screened at an acceptable time and cost. Several databases contain information on various aspects of biologically active substances. Simple statistical tools are difficult to use because of the enormous amount of information and complex data samples of molecules that are structurally heterogeneous recorded in these databases. Many techniques for capturing the biological similarity between a test compound and a known target ligand in LBVS have been established. However, despite the good performances of the above methods compared to their prior, especially when dealing with molecules that have homogeneous active structural elements, they are not satisfied when dealing with molecules that are structurally heterogeneous. Deep learning models have recently achieved considerable success in a variety of disciplines due to their powerful generalization and feature extraction capabilities. Also, the Siamese network has been used in similarity models for more complicated data samples, especially with heterogeneous data samples. The main aim of this study is to enhance the performance of similarity searching, especially with molecules that are structurally heterogeneous. The Siamese architecture will be enhanced using two similarity distance layers with one fusion layer to further improve the similarity measurements between molecules and then adding many layers after the fusion layer for some models to improve the retrieval recall. In this architecture, several methods of deep learning have been used, which are long short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural network-one dimension (CNN1D), and convolutional neural network-two dimensions (CNN2D). A series of experiments have been carried out on real-world data sets, and the results have shown that the proposed methods outperformed the existing methods.
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Affiliation(s)
- Mohammed Khaldoon Altalib
- School
of Computing, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
- Computer
Science Department, College of Education for Pure Sciences, University of Mosul, 41002 Mosul, Iraq
| | - Naomie Salim
- School
of Computing, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
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131
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Lin E, Lin CH, Lane HY. De Novo Peptide and Protein Design Using Generative Adversarial Networks: An Update. J Chem Inf Model 2022; 62:761-774. [DOI: 10.1021/acs.jcim.1c01361] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Eugene Lin
- Department of Biostatistics, University of Washington, Seattle, Washington 98195, United States
- Department of Electrical & Computer Engineering, University of Washington, Seattle, Washington 98195, United States
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
| | - Chieh-Hsin Lin
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
- Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan
- School of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
| | - Hsien-Yuan Lane
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
- Department of Psychiatry, China Medical University Hospital, Taichung 40447, Taiwan
- Brain Disease Research Center, China Medical University Hospital, Taichung 40447, Taiwan
- Department of Psychology, College of Medical and Health Sciences, Asia University, Taichung 41354, Taiwan
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Bernstam EV, Shireman PK, Meric‐Bernstam F, N. Zozus M, Jiang X, Brimhall BB, Windham AK, Schmidt S, Visweswaran S, Ye Y, Goodrum H, Ling Y, Barapatre S, Becich MJ. Artificial intelligence in clinical and translational science: Successes, challenges and opportunities. Clin Transl Sci 2022; 15:309-321. [PMID: 34706145 PMCID: PMC8841416 DOI: 10.1111/cts.13175] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 10/01/2021] [Indexed: 01/12/2023] Open
Abstract
Artificial intelligence (AI) is transforming many domains, including finance, agriculture, defense, and biomedicine. In this paper, we focus on the role of AI in clinical and translational research (CTR), including preclinical research (T1), clinical research (T2), clinical implementation (T3), and public (or population) health (T4). Given the rapid evolution of AI in CTR, we present three complementary perspectives: (1) scoping literature review, (2) survey, and (3) analysis of federally funded projects. For each CTR phase, we addressed challenges, successes, failures, and opportunities for AI. We surveyed Clinical and Translational Science Award (CTSA) hubs regarding AI projects at their institutions. Nineteen of 63 CTSA hubs (30%) responded to the survey. The most common funding source (48.5%) was the federal government. The most common translational phase was T2 (clinical research, 40.2%). Clinicians were the intended users in 44.6% of projects and researchers in 32.3% of projects. The most common computational approaches were supervised machine learning (38.6%) and deep learning (34.2%). The number of projects steadily increased from 2012 to 2020. Finally, we analyzed 2604 AI projects at CTSA hubs using the National Institutes of Health Research Portfolio Online Reporting Tools (RePORTER) database for 2011-2019. We mapped available abstracts to medical subject headings and found that nervous system (16.3%) and mental disorders (16.2) were the most common topics addressed. From a computational perspective, big data (32.3%) and deep learning (30.0%) were most common. This work represents a snapshot in time of the role of AI in the CTSA program.
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Affiliation(s)
- Elmer V. Bernstam
- School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTexasUSA
- Division of General Internal MedicineDepartment of Internal MedicineMcGovern Medical SchoolThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Paula K. Shireman
- Departments of Surgery and MicrobiologyImmunology & Molecular GeneticsUniversity of Texas Health San AntonioSan AntonioTexasUSA
- University HealthSan AntonioTexasUSA
- South Texas Veterans Health Care SystemSan AntonioTexasUSA
| | - Funda Meric‐Bernstam
- Department of Investigational Cancer TherapeuticsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Meredith N. Zozus
- Division of Clinical Research InformaticsDepartment of Population Health SciencesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Xiaoqian Jiang
- School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Bradley B. Brimhall
- University HealthSan AntonioTexasUSA
- Department of PathologyUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Ashley K. Windham
- University HealthSan AntonioTexasUSA
- Department of PathologyUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Susanne Schmidt
- Department of Population Health SciencesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Shyam Visweswaran
- Department of Biomedical InformaticsUniversity of Pittsburgh School of MedicinePittsburghPennsylvaniaUSA
| | - Ye Ye
- Department of Biomedical InformaticsUniversity of Pittsburgh School of MedicinePittsburghPennsylvaniaUSA
| | - Heath Goodrum
- School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Yaobin Ling
- School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Seemran Barapatre
- Department of Biomedical InformaticsUniversity of Pittsburgh School of MedicinePittsburghPennsylvaniaUSA
| | - Michael J. Becich
- Department of Biomedical InformaticsUniversity of Pittsburgh School of MedicinePittsburghPennsylvaniaUSA
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133
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Alves LA, Ferreira NCDS, Maricato V, Alberto AVP, Dias EA, Jose Aguiar Coelho N. Graph Neural Networks as a Potential Tool in Improving Virtual Screening Programs. Front Chem 2022; 9:787194. [PMID: 35127645 PMCID: PMC8811035 DOI: 10.3389/fchem.2021.787194] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 12/10/2021] [Indexed: 11/23/2022] Open
Abstract
Despite the increasing number of pharmaceutical companies, university laboratories and funding, less than one percent of initially researched drugs enter the commercial market. In this context, virtual screening (VS) has gained much attention due to several advantages, including timesaving, reduced reagent and consumable costs and the performance of selective analyses regarding the affinity between test molecules and pharmacological targets. Currently, VS is based mainly on algorithms that apply physical and chemistry principles and quantum mechanics to estimate molecule affinities and conformations, among others. Nevertheless, VS has not reached the expected results concerning the improvement of market-approved drugs, comprising less than twenty drugs that have reached this goal to date. In this context, graph neural networks (GNN), a recent deep-learning subtype, may comprise a powerful tool to improve VS results concerning natural products that may be used both simultaneously with standard algorithms or isolated. This review discusses the pros and cons of GNN applied to VS and the future perspectives of this learnable algorithm, which may revolutionize drug discovery if certain obstacles concerning spatial coordinates and adequate datasets, among others, can be overcome.
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Affiliation(s)
- Luiz Anastacio Alves
- Laboratory of Cellular Communication, Oswaldo Cruz Institute – Fiocruz, Rio de Janeiro, Brazil
| | | | - Victor Maricato
- Laboratory of Cellular Communication, Oswaldo Cruz Institute – Fiocruz, Rio de Janeiro, Brazil
| | | | - Evellyn Araujo Dias
- Laboratory of Cellular Communication, Oswaldo Cruz Institute – Fiocruz, Rio de Janeiro, Brazil
| | - Nt Jose Aguiar Coelho
- National Institute of Industrial Property - INPI and Veiga de Almeida University - UVA, Rio de Janeiro, Brazil
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134
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Meng Y, Lu C, Jin M, Xu J, Zeng X, Yang J. A weighted bilinear neural collaborative filtering approach for drug repositioning. Brief Bioinform 2022; 23:6510159. [PMID: 35039838 DOI: 10.1093/bib/bbab581] [Citation(s) in RCA: 58] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/25/2021] [Accepted: 12/19/2021] [Indexed: 02/07/2023] Open
Abstract
Drug repositioning is an efficient and promising strategy for traditional drug discovery and development. Many research efforts are focused on utilizing deep-learning approaches based on a heterogeneous network for modeling complex drug-disease associations. Similar to traditional latent factor models, which directly factorize drug-disease associations, they assume the neighbors are independent of each other in the network and thus tend to be ineffective to capture localized information. In this study, we propose a novel neighborhood and neighborhood interaction-based neural collaborative filtering approach (called DRWBNCF) to infer novel potential drugs for diseases. Specifically, we first construct three networks, including the known drug-disease association network, the drug-drug similarity and disease-disease similarity networks (using the nearest neighbors). To take the advantage of localized information in the three networks, we then design an integration component by proposing a new weighted bilinear graph convolution operation to integrate the information of the known drug-disease association, the drug's and disease's neighborhood and neighborhood interactions into a unified representation. Lastly, we introduce a prediction component, which utilizes the multi-layer perceptron optimized by the α-balanced focal loss function and graph regularization to model the complex drug-disease associations. Benchmarking comparisons on three datasets verified the effectiveness of DRWBNCF for drug repositioning. Importantly, the unknown drug-disease associations predicted by DRWBNCF were validated against clinical trials and three authoritative databases and we listed several new DRWBNCF-predicted potential drugs for breast cancer (e.g. valrubicin and teniposide) and small cell lung cancer (e.g. valrubicin and cytarabine).
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Affiliation(s)
- Yajie Meng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, 410082, China
| | - Changcheng Lu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, 410082, China
| | - Min Jin
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, 410082, China
| | - Junlin Xu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, 410082, China
| | - Xiangxiang Zeng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, 410082, China
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135
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Watson ER, Taherian Fard A, Mar JC. Computational Methods for Single-Cell Imaging and Omics Data Integration. Front Mol Biosci 2022; 8:768106. [PMID: 35111809 PMCID: PMC8801747 DOI: 10.3389/fmolb.2021.768106] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 11/29/2021] [Indexed: 12/12/2022] Open
Abstract
Integrating single cell omics and single cell imaging allows for a more effective characterisation of the underlying mechanisms that drive a phenotype at the tissue level, creating a comprehensive profile at the cellular level. Although the use of imaging data is well established in biomedical research, its primary application has been to observe phenotypes at the tissue or organ level, often using medical imaging techniques such as MRI, CT, and PET. These imaging technologies complement omics-based data in biomedical research because they are helpful for identifying associations between genotype and phenotype, along with functional changes occurring at the tissue level. Single cell imaging can act as an intermediary between these levels. Meanwhile new technologies continue to arrive that can be used to interrogate the genome of single cells and its related omics datasets. As these two areas, single cell imaging and single cell omics, each advance independently with the development of novel techniques, the opportunity to integrate these data types becomes more and more attractive. This review outlines some of the technologies and methods currently available for generating, processing, and analysing single-cell omics- and imaging data, and how they could be integrated to further our understanding of complex biological phenomena like ageing. We include an emphasis on machine learning algorithms because of their ability to identify complex patterns in large multidimensional data.
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Affiliation(s)
| | - Atefeh Taherian Fard
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, Australia
| | - Jessica Cara Mar
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, Australia
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136
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Lu W, Zhou N, Ding Y, Wu H, Zhang Y, Fu Q, Li H. Application of DNA-Binding Protein Prediction Based on Graph Convolutional Network and Contact Map. BIOMED RESEARCH INTERNATIONAL 2022; 2022:9044793. [PMID: 35083336 PMCID: PMC8786515 DOI: 10.1155/2022/9044793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 12/24/2021] [Indexed: 11/24/2022]
Abstract
DNA contains the genetic information for the synthesis of proteins and RNA, and it is an indispensable substance in living organisms. DNA-binding proteins are an enzyme, which can bind with DNA to produce complex proteins, and play an important role in the functions of a variety of biological molecules. With the continuous development of deep learning, the introduction of deep learning into DNA-binding proteins for prediction is conducive to improving the speed and accuracy of DNA-binding protein recognition. In this study, the features and structures of proteins were used to obtain their representations through graph convolutional networks. A protein prediction model based on graph convolutional network and contact map was proposed. The method had some advantages by testing various indexes of PDB14189 and PDB2272 on the benchmark dataset.
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Affiliation(s)
- Weizhong Lu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China
- Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, China
| | - Nan Zhou
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China
| | - Yijie Ding
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China
| | - Hongjie Wu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China
| | - Yu Zhang
- Suzhou Industrial Park Institute of Services Outsourcing, Suzhou, China
| | - Qiming Fu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China
| | - Haiou Li
- Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, China
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137
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Zhao Q, Zhao H, Zheng K, Wang J. HyperAttentionDTI: improving drug-protein interaction prediction by sequence-based deep learning with attention mechanism. Bioinformatics 2022; 38:655-662. [PMID: 34664614 DOI: 10.1093/bioinformatics/btab715] [Citation(s) in RCA: 51] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 09/24/2021] [Accepted: 10/13/2021] [Indexed: 02/06/2023] Open
Abstract
MOTIVATION Identifying drug-target interactions (DTIs) is a crucial step in drug repurposing and drug discovery. Accurately identifying DTIs in silico can significantly shorten development time and reduce costs. Recently, many sequence-based methods are proposed for DTI prediction and improve performance by introducing the attention mechanism. However, these methods only model single non-covalent inter-molecular interactions among drugs and proteins and ignore the complex interaction between atoms and amino acids. RESULTS In this article, we propose an end-to-end bio-inspired model based on the convolutional neural network (CNN) and attention mechanism, named HyperAttentionDTI, for predicting DTIs. We use deep CNNs to learn the feature matrices of drugs and proteins. To model complex non-covalent inter-molecular interactions among atoms and amino acids, we utilize the attention mechanism on the feature matrices and assign an attention vector to each atom or amino acid. We evaluate HpyerAttentionDTI on three benchmark datasets and the results show that our model achieves significantly improved performance compared with the state-of-the-art baselines. Moreover, a case study on the human Gamma-aminobutyric acid receptors confirm that our model can be used as a powerful tool to predict DTIs. AVAILABILITY AND IMPLEMENTATION The codes of our model are available at https://github.com/zhaoqichang/HpyerAttentionDTI and https://zenodo.org/record/5039589. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Qichang Zhao
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Haochen Zhao
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Kai Zheng
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Jianxin Wang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
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138
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Palamarchuk IV, Shulgau ZT, Dautov AY, Sergazy SD, Kulakov IV. Design, synthesis, spectroscopic characterization, computational analysis, and in vitro α-amylase and α-glucosidase evaluation of 3-aminopyridin-2(1 H)-one based novel monothiooxamides and 1,3,4-thiadiazoles. Org Biomol Chem 2022; 20:8962-8976. [DOI: 10.1039/d2ob01772e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
On the basis of biologically active 3-aminopyridin-2(1H)-ones, chemical modification of derivatives of the corresponding monothiooxamides, thiohydrazides, and conjugated 1,3,4-thiadiazole derivatives has been carried out for the first time.
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Affiliation(s)
- Irina V. Palamarchuk
- Tyumen State University, Institute of Chemistry, 15a Perekopskaya St., Tyumen 625003, Russia
| | - Zarina T. Shulgau
- National Center for Biotechnology, 13/5 Kurgalzhynskoe road, Nur-Sultan, 010000, Kazakhstan
| | - Adilet Y. Dautov
- National Center for Biotechnology, 13/5 Kurgalzhynskoe road, Nur-Sultan, 010000, Kazakhstan
| | - Shynggys D. Sergazy
- National Center for Biotechnology, 13/5 Kurgalzhynskoe road, Nur-Sultan, 010000, Kazakhstan
| | - Ivan V. Kulakov
- Tyumen State University, Institute of Chemistry, 15a Perekopskaya St., Tyumen 625003, Russia
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139
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Molecular docking and molecular dynamic simulation approaches for drug development and repurposing of drugs for severe acute respiratory syndrome-Coronavirus-2. COMPUTATIONAL APPROACHES FOR NOVEL THERAPEUTIC AND DIAGNOSTIC DESIGNING TO MITIGATE SARS-COV-2 INFECTION 2022. [PMCID: PMC9300476 DOI: 10.1016/b978-0-323-91172-6.00007-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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140
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Mohseniabbasabadi T, Behboodyzad F, Abolhasani Zadeh F, Balali E. Vismodegib anticancer drug: Analyzing electronic and structural features and examining biological activities. MAIN GROUP CHEMISTRY 2021. [DOI: 10.3233/mgc-210160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Vismodegib (Vis) is an anticancer drug, in which its electronic and structural features were examined in this work. To this aim, the chlorine atoms of original Vis model were substituted by other fluorine, bromine, and iodine halogen atoms yielding F-Vis, Br-Vis, and I-Vis in addition to the original Cl-Vis model. The models were optimized by performing quantum chemical calculations and their interactions with the smoothened (SMO) target were examined by performing molecular docking simulations. The results indicated that the stabilized structures of halogenated Vis models were achievable and their features indicated the dominant role of halogen atoms for their participation in interactions with other substances. Based on the obtained results, Br-Vis model was seen suitable for participating in interaction with the SMO target even better than the original Vis model. The hypothesis of this work was affirmed by employing the in silico approach for analyzing the features of singular ligands and for evaluating their biological functions.
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Affiliation(s)
- Tahereh Mohseniabbasabadi
- Department of Organic Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Farnoosh Behboodyzad
- Department of Organic Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | | | - Ebrahim Balali
- Department of Organic Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
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141
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Cetin-Atalay R, Kahraman DC, Nalbat E, Rifaioglu AS, Atakan A, Donmez A, Atas H, Atalay MV, Acar AC, Doğan T. Data Centric Molecular Analysis and Evaluation of Hepatocellular Carcinoma Therapeutics Using Machine Intelligence-Based Tools. J Gastrointest Cancer 2021; 52:1266-1276. [PMID: 34910274 DOI: 10.1007/s12029-021-00768-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/13/2021] [Indexed: 10/19/2022]
Abstract
PURPOSE Computational approaches have been used at different stages of drug development with the purpose of decreasing the time and cost of conventional experimental procedures. Lately, techniques mainly developed and applied in the field of artificial intelligence (AI), have been transferred to different application domains such as biomedicine. METHODS In this study, we conducted an investigative analysis via data-driven evaluation of potential hepatocellular carcinoma (HCC) therapeutics in the context of AI-assisted drug discovery/repurposing. First, we discussed basic concepts, computational approaches, databases, modeling approaches, and featurization techniques in drug discovery/repurposing. In the analysis part, we automatically integrated HCC-related biological entities such as genes/proteins, pathways, phenotypes, drugs/compounds, and other diseases with similar implications, and represented these heterogeneous relationships via a knowledge graph using the CROssBAR system. RESULTS Following the system-level evaluation and selection of critical genes/proteins and pathways to target, our deep learning-based drug/compound-target protein interaction predictors DEEPScreen and MDeePred have been employed for predicting new bioactive drugs and compounds for these critical targets. Finally, we embedded ligands of selected HCC-associated proteins which had a significant enrichment with the CROssBAR system into a 2-D space to identify and repurpose small molecule inhibitors as potential drug candidates based on their molecular similarities to known HCC drugs. CONCLUSIONS We expect that these series of data-driven analyses can be used as a roadmap to propose early-stage potential inhibitors (from database-scale sets of compounds) to both HCC and other complex diseases, which may subsequently be analyzed with more targeted in silico and experimental approaches.
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Affiliation(s)
- Rengul Cetin-Atalay
- Section of Pulmonary and Critical Care Medicine, University of Chicago, Chicago, IL, 60637, USA.
| | - Deniz Cansen Kahraman
- Cancer Systems Biology Laboratory, Graduate School of Informatics, METU, Ankara, 06800, Turkey.
| | - Esra Nalbat
- Cancer Systems Biology Laboratory, Graduate School of Informatics, METU, Ankara, 06800, Turkey
| | - Ahmet Sureyya Rifaioglu
- Department of Computer Engineering, Iskenderun Technical University, Iskenderun, Hatay, 31200, Turkey.,Department of Computer Engineering, METU, Ankara, 06800, Turkey
| | - Ahmet Atakan
- Department of Computer Engineering, METU, Ankara, 06800, Turkey.,Department of Computer Engineering, EBYU, Ankara, 24002, Turkey
| | - Ataberk Donmez
- Department of Computer Engineering, METU, Ankara, 06800, Turkey.,Department of Computer Science, University of Maryland, College Park, MD, 20742, USA
| | - Heval Atas
- Cancer Systems Biology Laboratory, Graduate School of Informatics, METU, Ankara, 06800, Turkey
| | - M Volkan Atalay
- Cancer Systems Biology Laboratory, Graduate School of Informatics, METU, Ankara, 06800, Turkey.,Department of Computer Engineering, METU, Ankara, 06800, Turkey
| | - Aybar C Acar
- Cancer Systems Biology Laboratory, Graduate School of Informatics, METU, Ankara, 06800, Turkey
| | - Tunca Doğan
- Cancer Systems Biology Laboratory, Graduate School of Informatics, METU, Ankara, 06800, Turkey. .,Department of Computer Engineering, Hacettepe University, Ankara, 06800, Turkey.
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142
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In search of SARS CoV-2 replication inhibitors: Virtual screening, molecular dynamics simulations and ADMET analysis. J Mol Struct 2021; 1246:131190. [PMID: 34334813 PMCID: PMC8313085 DOI: 10.1016/j.molstruc.2021.131190] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 07/11/2021] [Accepted: 07/24/2021] [Indexed: 01/18/2023]
Abstract
Severe acute respiratory syndrome has relapsed recently as novel coronavirus causing a life threat to the entire world in the absence of an effective therapy. To hamper the replication of the deadly SARS CoV-2 inside the host cells, systematic in silico virtual screening of total 267,324 ligands from Asinex EliteSynergy and BioDesign libraries has been performed using AutoDock Vina against RdRp. The molecular modeling studies revealed the identification of twenty-one macrocyclic hits (2-22) with better binding energy than remdesivir (1), marketed SARS CoV-2 inhibitor. Further, the analysis using rules for drug-likeness and their ADMET profile revealed the candidature of these hits due to superior oral bioavailability and druggability. Further, the MD simulation studies of top two hits (2 and 3) performed using GROMACS 2020.1 for 10 ns revealed their stability into the docked complexes. These results provide an important breakthrough in the design of macrocyclic hits as SARS CoV-2 RNA replicase inhibitor.
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Key Words
- ACE2, angiotensin converting enzyme 2
- ADMET assay
- ADMET, absorption, distribution, metabolism, excretion and toxicity
- BBB, blood-brain barrier
- BOILED, brain or intestinal estimated permeation method
- COVID-19
- COVID-19, corona virus disease 2019
- E, envelope protein
- FDA, food and drugs administration
- HBA, hydrogen bond acceptor
- HBD, hydrogen bond donor
- HERG, human ether-a-go-go-related gene
- LOAEL, oral rat chronic toxicity
- M, membrane protein
- MD simulations
- MD, molecular dynamics
- Molecular docking
- N, nucleocapsid protein
- NSPs, non-structural proteins
- RdRp
- RdRp, RNA dependent RNA polymerase
- S, spike glycoprotein
- SARS CoV-2
- SARS CoV-2, severe acute respiratory syndrome 2
- UTR, untranslated region
- WHO, world health organization
- pp1a/b, polyproteins
- ssRNA, single stranded ribonucleic acid
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143
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Abdulhakeem Mansour Alhasbary A, Hashimah Ahamed Hassain Malim N. Turbo Similarity Searching: Effect of Partial Ranking and Fusion Rules on ChEMBL Database. Mol Inform 2021; 41:e2100106. [PMID: 34878229 DOI: 10.1002/minf.202100106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 11/25/2021] [Indexed: 11/08/2022]
Abstract
Turbo Similarity Searching (TSS) is the simplest and most recent chemical similarity searching (SS) approach, which improves the effectiveness of SS by performing a multi-target searching. TSS has four important elements, namely structural representation, similarity coefficient, number of nearest neighbours (NNs), and fusion rule, and any changes in these elements could affect the TSS results. A previous study suggested the advantage of using large numbers of reference compounds with small fractions of the database structures to obtain a better recall in group fusion. Therefore, this study aims to investigate the effect of partial ranking on TSS utilising different fusion rules and different numbers of NNs on the ChEMBL database and to evaluate whether these observations hold in TSS. Furthermore, the objective is to observe the effect of the indirect relationship feature of TSS on the partial ranking investigation. The results showed that the effect of using partial ranking on TSS was significant. This study also found that the performance of TSS improved as the database proportions used in the fusion process decreased and by using a small number of NNs. In addition, fusion rules based on reciprocal rank positions (RKP), maximum similarity score (sMAX), and sMNZ were superior to all the other fusion rules.
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144
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Kessler A, Kouznetsova VL, Tsigelny IF. Targeting Epigenetic Regulators Using Machine Learning: Potential Sirtuin 2 Inhibitors. JOURNAL OF COMPUTATIONAL BIOPHYSICS AND CHEMISTRY 2021. [DOI: 10.1142/s2737416521500526] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Sirtuin 2 (SIRT2) is a nicotinamide adenine dinucleotide (NAD+)-dependent deacetylase that has been identified as a target for many diseases, including Parkinson’s disease (PD) and leukemia. Using 234 SIRT2 inhibitors from the ZINC15 database, we generated molecular descriptors with PaDEL and constructed a machine-learning (ML) model for the binary classification of SIRT2 inhibitors. To predict compounds with novel inhibitory mechanisms, we then applied the model on the ZINC15/FDA subset, yielding 107 potential SIRT2 inhibitors. For validation of these substances, we employed the binding analysis software AutoDock Vina to perform virtual screening, with which 43 compounds were considered best inhibitors at the [Formula: see text][Formula: see text]kcal/mol binding affinity threshold. Our results demonstrate the potential of ligand-based (LB) ML techniques in conjunction with receptor-based virtual screening (RBVS) to facilitate the drug discovery or repurposing.
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Affiliation(s)
- Andrew Kessler
- REHS program, San Diego Supercomputer Center, UC San Diego, California, USA
| | | | - Igor F. Tsigelny
- San Diego Supercomputer Center, UC San Diego, California, USA
- BiAna, San Diego, California, USA
- Department of Neurosciences, UC San Diego, California, USA
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145
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Harigua-Souiai E, Heinhane MM, Abdelkrim YZ, Souiai O, Abdeljaoued-Tej I, Guizani I. Deep Learning Algorithms Achieved Satisfactory Predictions When Trained on a Novel Collection of Anticoronavirus Molecules. Front Genet 2021; 12:744170. [PMID: 34912370 PMCID: PMC8667578 DOI: 10.3389/fgene.2021.744170] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 09/30/2021] [Indexed: 12/26/2022] Open
Abstract
Drug discovery and repurposing against COVID-19 is a highly relevant topic with huge efforts dedicated to delivering novel therapeutics targeting SARS-CoV-2. In this context, computer-aided drug discovery is of interest in orienting the early high throughput screenings and in optimizing the hit identification rate. We herein propose a pipeline for Ligand-Based Drug Discovery (LBDD) against SARS-CoV-2. Through an extensive search of the literature and multiple steps of filtering, we integrated information on 2,610 molecules having a validated effect against SARS-CoV and/or SARS-CoV-2. The chemical structures of these molecules were encoded through multiple systems to be readily useful as input to conventional machine learning (ML) algorithms or deep learning (DL) architectures. We assessed the performances of seven ML algorithms and four DL algorithms in achieving molecule classification into two classes: active and inactive. The Random Forests (RF), Graph Convolutional Network (GCN), and Directed Acyclic Graph (DAG) models achieved the best performances. These models were further optimized through hyperparameter tuning and achieved ROC-AUC scores through cross-validation of 85, 83, and 79% for RF, GCN, and DAG models, respectively. An external validation step on the FDA-approved drugs collection revealed a superior potential of DL algorithms to achieve drug repurposing against SARS-CoV-2 based on the dataset herein presented. Namely, GCN and DAG achieved more than 50% of the true positive rate assessed on the confirmed hits of a PubChem bioassay.
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Affiliation(s)
- Emna Harigua-Souiai
- Laboratory of Molecular Epidemiology and Experimental Pathology-LR16IPT04, Institut Pasteur de Tunis, Université de Tunis El Manar, Tunis, Tunisia
| | - Mohamed Mahmoud Heinhane
- Laboratory of Molecular Epidemiology and Experimental Pathology-LR16IPT04, Institut Pasteur de Tunis, Université de Tunis El Manar, Tunis, Tunisia
| | - Yosser Zina Abdelkrim
- Laboratory of Molecular Epidemiology and Experimental Pathology-LR16IPT04, Institut Pasteur de Tunis, Université de Tunis El Manar, Tunis, Tunisia
| | - Oussama Souiai
- Laboratory of BioInformatics BioMathematics and BioStatistics (BIMS)-LR20IPT09, Institut Pasteur de Tunis, University of Tunis El Manar, Tunis, Tunisia
| | - Ines Abdeljaoued-Tej
- Laboratory of BioInformatics BioMathematics and BioStatistics (BIMS)-LR20IPT09, Institut Pasteur de Tunis, University of Tunis El Manar, Tunis, Tunisia
- Engineering School of Statistics and Information Analysis, University of Carthage, Ariana, Tunisia
| | - Ikram Guizani
- Laboratory of Molecular Epidemiology and Experimental Pathology-LR16IPT04, Institut Pasteur de Tunis, Université de Tunis El Manar, Tunis, Tunisia
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146
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Lv Q, Chen G, Zhao L, Zhong W, Yu-Chian Chen C. Mol2Context-vec: learning molecular representation from context awareness for drug discovery. Brief Bioinform 2021; 22:6357185. [PMID: 34428290 DOI: 10.1093/bib/bbab317] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 07/15/2021] [Accepted: 07/21/2021] [Indexed: 11/14/2022] Open
Abstract
With the rapid development of proteomics and the rapid increase of target molecules for drug action, computer-aided drug design (CADD) has become a basic task in drug discovery. One of the key challenges in CADD is molecular representation. High-quality molecular expression with chemical intuition helps to promote many boundary problems of drug discovery. At present, molecular representation still faces several urgent problems, such as the polysemy of substructures and unsmooth information flow between atomic groups. In this research, we propose a deep contextualized Bi-LSTM architecture, Mol2Context-vec, which can integrate different levels of internal states to bring dynamic representations of molecular substructures. And the obtained molecular context representation can capture the interactions between any atomic groups, especially a pair of atomic groups that are topologically distant. Experiments show that Mol2Context-vec achieves state-of-the-art performance on multiple benchmark datasets. In addition, the visual interpretation of Mol2Context-vec is very close to the structural properties of chemical molecules as understood by humans. These advantages indicate that Mol2Context-vec can be used as a reliable and effective tool for molecular expression. Availability: The source code is available for download in https://github.com/lol88/Mol2Context-vec.
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Affiliation(s)
- Qiujie Lv
- School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, 510275, China
| | - Guanxing Chen
- School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, 510275, China
| | - Lu Zhao
- The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510655, China
| | - Weihe Zhong
- School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, 510275, China
| | - Calvin Yu-Chian Chen
- School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, 510275, 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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147
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Gaudelet T, Day B, Jamasb AR, Soman J, Regep C, Liu G, Hayter JBR, Vickers R, Roberts C, Tang J, Roblin D, Blundell TL, Bronstein MM, Taylor-King JP. Utilizing graph machine learning within drug discovery and development. Brief Bioinform 2021; 22:bbab159. [PMID: 34013350 PMCID: PMC8574649 DOI: 10.1093/bib/bbab159] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 04/01/2021] [Accepted: 04/05/2021] [Indexed: 12/15/2022] Open
Abstract
Graph machine learning (GML) is receiving growing interest within the pharmaceutical and biotechnology industries for its ability to model biomolecular structures, the functional relationships between them, and integrate multi-omic datasets - amongst other data types. Herein, we present a multidisciplinary academic-industrial review of the topic within the context of drug discovery and development. After introducing key terms and modelling approaches, we move chronologically through the drug development pipeline to identify and summarize work incorporating: target identification, design of small molecules and biologics, and drug repurposing. Whilst the field is still emerging, key milestones including repurposed drugs entering in vivo studies, suggest GML will become a modelling framework of choice within biomedical machine learning.
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Affiliation(s)
| | - Ben Day
- Relation Therapeutics, London, UK
- The Computer Laboratory, University of Cambridge, UK
| | - Arian R Jamasb
- Relation Therapeutics, London, UK
- The Computer Laboratory, University of Cambridge, UK
- Department of Biochemistry, University of Cambridge, UK
| | | | | | | | | | | | | | - Jian Tang
- Mila, the Quebec AI Institute, Canada
- HEC Montreal, Canada
| | - David Roblin
- Relation Therapeutics, London, UK
- Juvenescence, London, UK
- The Francis Crick Institute, London, UK
| | | | - Michael M Bronstein
- Relation Therapeutics, London, UK
- Department of Computing, Imperial College London, UK
- Twitter, UK
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148
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Zagidullin B, Wang Z, Guan Y, Pitkänen E, Tang J. Comparative analysis of molecular fingerprints in prediction of drug combination effects. Brief Bioinform 2021; 22:bbab291. [PMID: 34401895 PMCID: PMC8574997 DOI: 10.1093/bib/bbab291] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 06/01/2021] [Accepted: 07/07/2021] [Indexed: 12/18/2022] Open
Abstract
Application of machine and deep learning methods in drug discovery and cancer research has gained a considerable amount of attention in the past years. As the field grows, it becomes crucial to systematically evaluate the performance of novel computational solutions in relation to established techniques. To this end, we compare rule-based and data-driven molecular representations in prediction of drug combination sensitivity and drug synergy scores using standardized results of 14 high-throughput screening studies, comprising 64 200 unique combinations of 4153 molecules tested in 112 cancer cell lines. We evaluate the clustering performance of molecular representations and quantify their similarity by adapting the Centered Kernel Alignment metric. Our work demonstrates that to identify an optimal molecular representation type, it is necessary to supplement quantitative benchmark results with qualitative considerations, such as model interpretability and robustness, which may vary between and throughout preclinical drug development projects.
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Affiliation(s)
- B Zagidullin
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Finland
| | - Z Wang
- Department of Electrical Engineering & Computer Science, University of Michigan, Ann Arbor, USA
| | - Y Guan
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, USA
| | - E Pitkänen
- Institute for Molecular Medicine Finland (FIMM) & Applied Tumor Genomics Research Program, Research Programs Unit, University of Helsinki, Finland
| | - J Tang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Finland
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149
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Cheng Y, Gong Y, Liu Y, Song B, Zou Q. Molecular design in drug discovery: a comprehensive review of deep generative models. Brief Bioinform 2021; 22:6355420. [PMID: 34415297 DOI: 10.1093/bib/bbab344] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 07/19/2021] [Accepted: 08/04/2021] [Indexed: 12/22/2022] Open
Abstract
Deep generative models have been an upsurge in the deep learning community since they were proposed. These models are designed for generating new synthetic data including images, videos and texts by fitting the data approximate distributions. In the last few years, deep generative models have shown superior performance in drug discovery especially de novo molecular design. In this study, deep generative models are reviewed to witness the recent advances of de novo molecular design for drug discovery. In addition, we divide those models into two categories based on molecular representations in silico. Then these two classical types of models are reported in detail and discussed about both pros and cons. We also indicate the current challenges in deep generative models for de novo molecular design. De novo molecular design automatically is promising but a long road to be explored.
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Affiliation(s)
- Yu Cheng
- College of Information Science and Engineering, Hunan University, 2 Lushan S Rd, Yuelu District, 410086, Changsha, China
| | - Yongshun Gong
- School of Software, Shandong University, 250100, Jinan, China
| | - Yuansheng Liu
- College of Information Science and Engineering, Hunan University, 2 Lushan S Rd, Yuelu District, 410086, Changsha, China
| | - Bosheng Song
- College of Information Science and Engineering, Hunan University, 2 Lushan S Rd, Yuelu District, 410086, Changsha, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, 610054, Chengdu, China
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150
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Tayara H, Abdelbaky I, To Chong K. Recent omics-based computational methods for COVID-19 drug discovery and repurposing. Brief Bioinform 2021; 22:6355836. [PMID: 34423353 DOI: 10.1093/bib/bbab339] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 07/09/2021] [Indexed: 12/22/2022] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is the main reason for the increasing number of deaths worldwide. Although strict quarantine measures were followed in many countries, the disease situation is still intractable. Thus, it is needed to utilize all possible means to confront this pandemic. Therefore, researchers are in a race against the time to produce potential treatments to cure or reduce the increasing infections of COVID-19. Computational methods are widely proving rapid successes in biological related problems, including diagnosis and treatment of diseases. Many efforts in recent months utilized Artificial Intelligence (AI) techniques in the context of fighting the spread of COVID-19. Providing periodic reviews and discussions of recent efforts saves the time of researchers and helps to link their endeavors for a faster and efficient confrontation of the pandemic. In this review, we discuss the recent promising studies that used Omics-based data and utilized AI algorithms and other computational tools to achieve this goal. We review the established datasets and the developed methods that were basically directed to new or repurposed drugs, vaccinations and diagnosis. The tools and methods varied depending on the level of details in the available information such as structures, sequences or metabolic data.
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Affiliation(s)
- Hilal Tayara
- School of international Engineering and Science, Jeonbuk National University, Jeonju 54896, Republic of Korea
| | - Ibrahim Abdelbaky
- Artificial Intelligence Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, Jeollabukdo 54896, Republic of Korea.,Advances Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, Republic of Korea
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