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Bharadwaj S, Deepika K, Kumar A, Jaiswal S, Miglani S, Singh D, Fartyal P, Kumar R, Singh S, Singh MP, Gaidhane AM, Kumar B, Jha V. Exploring the Artificial Intelligence and Its Impact in Pharmaceutical Sciences: Insights Toward the Horizons Where Technology Meets Tradition. Chem Biol Drug Des 2024; 104:e14639. [PMID: 39396920 DOI: 10.1111/cbdd.14639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2024] [Revised: 09/03/2024] [Accepted: 09/24/2024] [Indexed: 10/15/2024]
Abstract
The technological revolutions in computers and the advancement of high-throughput screening technologies have driven the application of artificial intelligence (AI) for faster discovery of drug molecules with more efficiency, and cost-friendly finding of hit or lead molecules. The ability of software and network frameworks to interpret molecular structures' representations and establish relationships/correlations has enabled various research teams to develop numerous AI platforms for identifying new lead molecules or discovering new targets for already established drug molecules. The prediction of biological activity, ADME properties, and toxicity parameters in early stages have reduced the chances of failure and associated costs in later clinical stages, which was observed at a high rate in the tedious, expensive, and laborious drug discovery process. This review focuses on the different AI and machine learning (ML) techniques with their applications mainly focused on the pharmaceutical industry. The applications of AI frameworks in the identification of molecular target, hit identification/hit-to-lead optimization, analyzing drug-receptor interactions, drug repurposing, polypharmacology, synthetic accessibility, clinical trial design, and pharmaceutical developments are discussed in detail. We have also compiled the details of various startups in AI in this field. This review will provide a comprehensive analysis and outline various state-of-the-art AI/ML techniques to the readers with their framework applications. This review also highlights the challenges in this field, which need to be addressed for further success in pharmaceutical applications.
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Affiliation(s)
- Shruti Bharadwaj
- Center for SeNSE, Indian Institute of Technology Delhi (IIT), New Delhi, India
| | - Kumari Deepika
- Department of Computer Engineering, Pune Institute of Computer Technology, Pune, India
| | - Asim Kumar
- Amity Institute of Pharmacy (AIP), Amity University Haryana, Manesar, India
| | - Shivani Jaiswal
- Institute of Pharmaceutical Research, GLA University, Mathura, India
| | - Shaweta Miglani
- Department of Education, Central University of Punjab, Bathinda, India
| | - Damini Singh
- IES Institute of Pharmacy, IES University, Bhopal, Madhya Pradesh, India
| | - Prachi Fartyal
- Department of Mathematics, Govt PG College Bajpur (US Nagar), Bazpur, Uttarakhand, India
| | - Roshan Kumar
- Department of Microbiology, Graphic Era (Deemed to be University), Dehradun, India
- Department of Microbiology, Central University of Punjab, VPO-Ghudda, Punjab, India
| | - Shareen Singh
- Centre for Research Impact & Outcome, Chitkara College of Pharmacy, Chitkara University, Rajpura, Punjab, India
| | - Mahendra Pratap Singh
- Center for Global Health Research, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India
| | - Abhay M Gaidhane
- Jawaharlal Nehru Medical College, and Global Health Academy, School of Epidemiology and Public Health, Datta Meghe Institute of Higher Education, Wardha, India
| | - Bhupinder Kumar
- Department of Pharmaceutical Science, Hemvati Nandan Bahuguna Garhwal (A Central) University, Srinagar, Uttarakhand, India
| | - Vibhu Jha
- Institute of Cancer Therapeutics, School of Pharmacy and Medical Sciences, Faculty of Life Sciences, University of Bradford, Bradford, UK
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Bhattacharya D, Cassady HJ, Hickner MA, Reinhart WF. Large Language Models as Molecular Design Engines. J Chem Inf Model 2024. [PMID: 39231030 DOI: 10.1021/acs.jcim.4c01396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/06/2024]
Abstract
The design of small molecules is crucial for technological applications ranging from drug discovery to energy storage. Due to the vast design space available to modern synthetic chemistry, the community has increasingly sought to use data-driven and machine learning approaches to navigate this space. Although generative machine learning methods have recently shown potential for computational molecular design, their use is hindered by complex training procedures, and they often fail to generate valid and unique molecules. In this context, pretrained Large Language Models (LLMs) have emerged as potential tools for molecular design, as they appear to be capable of creating and modifying molecules based on simple instructions provided through natural language prompts. In this work, we show that the Claude 3 Opus LLM can read, write, and modify molecules according to prompts, with impressive 97% valid and unique molecules. By quantifying these modifications in a low-dimensional latent space, we systematically evaluate the model's behavior under different prompting conditions. Notably, the model is able to perform guided molecular generation when asked to manipulate the electronic structure of molecules using simple, natural-language prompts. Our findings highlight the potential of LLMs as powerful and versatile molecular design engines.
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Affiliation(s)
- Debjyoti Bhattacharya
- Materials Science and Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Harrison J Cassady
- Department of Chemical Engineering and Material Science, Michigan State University, East Lansing, Michigan 48824, United States
| | - Michael A Hickner
- Department of Chemical Engineering and Material Science, Michigan State University, East Lansing, Michigan 48824, United States
| | - Wesley F Reinhart
- Materials Science and Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Institute for Computational and Data Sciences, Pennsylvania State University, University Park, Pennsylvania 16802, United States
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Lavecchia A. Navigating the frontier of drug-like chemical space with cutting-edge generative AI models. Drug Discov Today 2024; 29:104133. [PMID: 39103144 DOI: 10.1016/j.drudis.2024.104133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 07/20/2024] [Accepted: 07/31/2024] [Indexed: 08/07/2024]
Abstract
Deep generative models (GMs) have transformed the exploration of drug-like chemical space (CS) by generating novel molecules through complex, nontransparent processes, bypassing direct structural similarity. This review examines five key architectures for CS exploration: recurrent neural networks (RNNs), variational autoencoders (VAEs), generative adversarial networks (GANs), normalizing flows (NF), and Transformers. It discusses molecular representation choices, training strategies for focused CS exploration, evaluation criteria for CS coverage, and related challenges. Future directions include refining models, exploring new notations, improving benchmarks, and enhancing interpretability to better understand biologically relevant molecular properties.
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Affiliation(s)
- Antonio Lavecchia
- 'Drug Discovery' Laboratory, Department of Pharmacy, University of Naples Federico II, I-80131 Naples, Italy.
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Zhang D, Wang J, Wu H, Chen Q. Picroside II disrupts IBDV replication via targeting VP1 polymerase. Vet Microbiol 2024; 296:110191. [PMID: 39032445 DOI: 10.1016/j.vetmic.2024.110191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2024] [Revised: 07/10/2024] [Accepted: 07/17/2024] [Indexed: 07/23/2024]
Abstract
Infectious bursal disease virus (IBDV) is a highly contagious virus with a dsRNA genome, predominantly infecting chickens and causing significant economic losses due to high mortality rates. The emergence of recombinant, novel variant, and highly virulent strains that evade current vaccines has led to frequent epidemics and outbreaks in the poultry industry. The lack of targeted antivirals for IBDV underscores the pressing requirement to develop potent therapeutic options. Within this framework, our research investigated the effectiveness of picroside II, a naturally derived iridoid glycoside, against viruses in DF-1 cells. Our findings demonstrate that picroside II significantly inhibits viral replication, with its efficacy increasing proportionally to the dosage administered. Through time-addition and antiviral duration analysis, we determined that picroside II therapeutically blocks IBDV replication, with its effects persisting for over 72 hours. Further investigation revealed that picroside II specifically inhibits the cellular replication stage of IBDV's lifecycle. Additionally, our findings indicate that picroside II impairs VP1 polymerase activity by binding to the active pocket, which significantly disrupts the interaction between VP1 and VP3. Mutations at three critical binding sites on VP1 not only impair virus replication but also hinder polymerase function and disrupt VP1-VP3 interactions. Collectively, these results demonstrate that picroside II, by inhibiting viral polymerase activity, represents a promising antiviral agent against IBDV.
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Affiliation(s)
- Donghu Zhang
- School of Pharmacy, China Medical University, Shenyang, Liaoning 110122, PR China; Department of Pharmacy, the First Hospital of China Medical University, Shenyang, Liaoning 110001, P R China
| | - Jing Wang
- Department of Ophthalmology, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, PR China
| | - Huansheng Wu
- Department of Veterinary Preventive Medicine, College of Animal Science and Technology, Jiangxi Agricultural University, Nanchang, Jiangxi 330045, PR China
| | - Qili Chen
- School of Pharmacy, China Medical University, Shenyang, Liaoning 110122, PR China.
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Wang J, Wang X, Chu Y, Li C, Li X, Meng X, Fang Y, No KT, Mao J, Zeng X. Exploring the Conformational Ensembles of Protein-Protein Complex with Transformer-Based Generative Model. J Chem Theory Comput 2024; 20:4469-4480. [PMID: 38816696 DOI: 10.1021/acs.jctc.4c00255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2024]
Abstract
Protein-protein interactions are the basis of many protein functions, and understanding the contact and conformational changes of protein-protein interactions is crucial for linking the protein structure to biological function. Although difficult to detect experimentally, molecular dynamics (MD) simulations are widely used to study the conformational ensembles and dynamics of protein-protein complexes, but there are significant limitations in sampling efficiency and computational costs. In this study, a generative neural network was trained on protein-protein complex conformations obtained from molecular simulations to directly generate novel conformations with physical realism. We demonstrated the use of a deep learning model based on the transformer architecture to explore the conformational ensembles of protein-protein complexes through MD simulations. The results showed that the learned latent space can be used to generate unsampled conformations of protein-protein complexes for obtaining new conformations complementing pre-existing ones, which can be used as an exploratory tool for the analysis and enhancement of molecular simulations of protein-protein complexes.
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Affiliation(s)
- Jianmin Wang
- The Interdisciplinary Graduate Program in Integrative Biotechnology, Yonsei University, Incheon 21983, Korea
| | - Xun Wang
- School of Computer Science and Technology, China University of Petroleum, Qingdao, Shandong 266580, P. R. China
- High Performance Computer Research Center, University of Chinese Academy of Sciences, Beijing 100190, P. R. China
| | - Yanyi Chu
- Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, United States
| | - Chunyan Li
- School of Informatics, Yunnan Normal University, Kunming, Yunnan 650500, P. R. China
| | - Xue Li
- School of Computer Science and Technology, China University of Petroleum, Qingdao, Shandong 266580, P. R. China
| | - Xiangyu Meng
- School of Computer Science and Technology, China University of Petroleum, Qingdao, Shandong 266580, P. R. China
| | - Yitian Fang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, P. R. China
| | - Kyoung Tai No
- The Interdisciplinary Graduate Program in Integrative Biotechnology, Yonsei University, Incheon 21983, Korea
| | - Jiashun Mao
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, Sichuan 646000, P. R. China
| | - Xiangxiang Zeng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, P. R. China
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Luong KD, Singh A. Application of Transformers in Cheminformatics. J Chem Inf Model 2024; 64:4392-4409. [PMID: 38815246 PMCID: PMC11167597 DOI: 10.1021/acs.jcim.3c02070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 04/05/2024] [Accepted: 05/06/2024] [Indexed: 06/01/2024]
Abstract
By accelerating time-consuming processes with high efficiency, computing has become an essential part of many modern chemical pipelines. Machine learning is a class of computing methods that can discover patterns within chemical data and utilize this knowledge for a wide variety of downstream tasks, such as property prediction or substance generation. The complex and diverse chemical space requires complex machine learning architectures with great learning power. Recently, learning models based on transformer architectures have revolutionized multiple domains of machine learning, including natural language processing and computer vision. Naturally, there have been ongoing endeavors in adopting these techniques to the chemical domain, resulting in a surge of publications within a short period. The diversity of chemical structures, use cases, and learning models necessitate a comprehensive summarization of existing works. In this paper, we review recent innovations in adapting transformers to solve learning problems in chemistry. Because chemical data is diverse and complex, we structure our discussion based on chemical representations. Specifically, we highlight the strengths and weaknesses of each representation, the current progress of adapting transformer architectures, and future directions.
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Affiliation(s)
- Kha-Dinh Luong
- Department of Computer Science, University of California Santa Barbara, Santa Barbara, CA 93106, United States
| | - Ambuj Singh
- Department of Computer Science, University of California Santa Barbara, Santa Barbara, CA 93106, United States
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Merz KM, Wei GW, Zhu F. Editorial: Machine Learning in Bio-cheminformatics. J Chem Inf Model 2024; 64:2125-2128. [PMID: 38587006 DOI: 10.1021/acs.jcim.4c00444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Affiliation(s)
- Kenneth M Merz
- Department of Chemistry, Michigan State University, Lansing 48824, Michigan, United States
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, Lansing 48824, Michigan, United States
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
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Kyro GW, Morgunov A, Brent RI, Batista VS. ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation. J Chem Inf Model 2024; 64:653-665. [PMID: 38287889 DOI: 10.1021/acs.jcim.3c01456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2024]
Abstract
The incredible capabilities of generative artificial intelligence models have inevitably led to their application in the domain of drug discovery. Within this domain, the vastness of chemical space motivates the development of more efficient methods for identifying regions with molecules that exhibit desired characteristics. In this work, we present a computationally efficient active learning methodology and demonstrate its applicability to targeted molecular generation. When applied to c-Abl kinase, a protein with FDA-approved small-molecule inhibitors, the model learns to generate molecules similar to the inhibitors without prior knowledge of their existence and even reproduces two of them exactly. We also show that the methodology is effective for a protein without any commercially available small-molecule inhibitors, the HNH domain of the CRISPR-associated protein 9 (Cas9) enzyme. To facilitate implementation and reproducibility, we made all of our software available through the open-source ChemSpaceAL Python package.
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Affiliation(s)
- Gregory W Kyro
- Department of Chemistry, Yale University, New Haven, Connecticut 06511-8499, United States
| | - Anton Morgunov
- Department of Chemistry, Yale University, New Haven, Connecticut 06511-8499, United States
| | - Rafael I Brent
- Department of Chemistry, Yale University, New Haven, Connecticut 06511-8499, United States
| | - Victor S Batista
- Department of Chemistry, Yale University, New Haven, Connecticut 06511-8499, United States
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Gangwal A, Ansari A, Ahmad I, Azad AK, Kumarasamy V, Subramaniyan V, Wong LS. Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities. Front Pharmacol 2024; 15:1331062. [PMID: 38384298 PMCID: PMC10879372 DOI: 10.3389/fphar.2024.1331062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 01/17/2024] [Indexed: 02/23/2024] Open
Abstract
There are two main ways to discover or design small drug molecules. The first involves fine-tuning existing molecules or commercially successful drugs through quantitative structure-activity relationships and virtual screening. The second approach involves generating new molecules through de novo drug design or inverse quantitative structure-activity relationship. Both methods aim to get a drug molecule with the best pharmacokinetic and pharmacodynamic profiles. However, bringing a new drug to market is an expensive and time-consuming endeavor, with the average cost being estimated at around $2.5 billion. One of the biggest challenges is screening the vast number of potential drug candidates to find one that is both safe and effective. The development of artificial intelligence in recent years has been phenomenal, ushering in a revolution in many fields. The field of pharmaceutical sciences has also significantly benefited from multiple applications of artificial intelligence, especially drug discovery projects. Artificial intelligence models are finding use in molecular property prediction, molecule generation, virtual screening, synthesis planning, repurposing, among others. Lately, generative artificial intelligence has gained popularity across domains for its ability to generate entirely new data, such as images, sentences, audios, videos, novel chemical molecules, etc. Generative artificial intelligence has also delivered promising results in drug discovery and development. This review article delves into the fundamentals and framework of various generative artificial intelligence models in the context of drug discovery via de novo drug design approach. Various basic and advanced models have been discussed, along with their recent applications. The review also explores recent examples and advances in the generative artificial intelligence approach, as well as the challenges and ongoing efforts to fully harness the potential of generative artificial intelligence in generating novel drug molecules in a faster and more affordable manner. Some clinical-level assets generated form generative artificial intelligence have also been discussed in this review to show the ever-increasing application of artificial intelligence in drug discovery through commercial partnerships.
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Affiliation(s)
- Amit Gangwal
- Department of Natural Product Chemistry, Shri Vile Parle Kelavani Mandal’s Institute of Pharmacy, Dhule, Maharashtra, India
| | - Azim Ansari
- Computer Aided Drug Design Center Shri Vile Parle Kelavani Mandal’s Institute of Pharmacy, Dhule, Maharashtra, India
| | - Iqrar Ahmad
- Department of Pharmaceutical Chemistry, Prof. Ravindra Nikam College of Pharmacy, Dhule, India
| | - Abul Kalam Azad
- Faculty of Pharmacy, University College of MAIWP International, Batu Caves, Malaysia
| | - Vinoth Kumarasamy
- Department of Parasitology and Medical Entomology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras, Malaysia
| | - Vetriselvan Subramaniyan
- Pharmacology Unit, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Selangor, Malaysia
- School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, Punjab, India
| | - Ling Shing Wong
- Faculty of Health and Life Sciences, INTI International University, Nilai, Malaysia
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Zhang Y, Liu C, Liu M, Liu T, Lin H, Huang CB, Ning L. Attention is all you need: utilizing attention in AI-enabled drug discovery. Brief Bioinform 2023; 25:bbad467. [PMID: 38189543 PMCID: PMC10772984 DOI: 10.1093/bib/bbad467] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 11/03/2023] [Accepted: 11/25/2023] [Indexed: 01/09/2024] Open
Abstract
Recently, attention mechanism and derived models have gained significant traction in drug development due to their outstanding performance and interpretability in handling complex data structures. This review offers an in-depth exploration of the principles underlying attention-based models and their advantages in drug discovery. We further elaborate on their applications in various aspects of drug development, from molecular screening and target binding to property prediction and molecule generation. Finally, we discuss the current challenges faced in the application of attention mechanisms and Artificial Intelligence technologies, including data quality, model interpretability and computational resource constraints, along with future directions for research. Given the accelerating pace of technological advancement, we believe that attention-based models will have an increasingly prominent role in future drug discovery. We anticipate that these models will usher in revolutionary breakthroughs in the pharmaceutical domain, significantly accelerating the pace of drug development.
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Affiliation(s)
- Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Caiqi Liu
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, No.150 Haping Road, Nangang District, Harbin, Heilongjiang 150081, China
- Key Laboratory of Molecular Oncology of Heilongjiang Province, No.150 Haping Road, Nangang District, Harbin, Heilongjiang 150081, China
| | - Mujiexin Liu
- Chongqing Key Laboratory of Sichuan-Chongqing Co-construction for Diagnosis and Treatment of Infectious Diseases Integrated Traditional Chinese and Western Medicine, College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Tianyuan Liu
- Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Japan
| | - Hao Lin
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Cheng-Bing Huang
- School of Computer Science and Technology, Aba Teachers University, Aba, China
| | - Lin Ning
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu 611844, China
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