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Asediya VS, Anjaria PA, Mathakiya RA, Koringa PG, Nayak JB, Bisht D, Fulmali D, Patel VA, Desai DN. Vaccine development using artificial intelligence and machine learning: A review. Int J Biol Macromol 2024:136643. [PMID: 39426778 DOI: 10.1016/j.ijbiomac.2024.136643] [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: 06/01/2024] [Revised: 09/30/2024] [Accepted: 10/15/2024] [Indexed: 10/21/2024]
Abstract
The COVID-19 pandemic has underscored the critical importance of effective vaccines, yet their development is a challenging and demanding process. It requires identifying antigens that elicit protective immunity, selecting adjuvants that enhance immunogenicity, and designing delivery systems that ensure optimal efficacy. Artificial intelligence (AI) can facilitate this process by using machine learning methods to analyze large and diverse datasets, suggest novel vaccine candidates, and refine their design and predict their performance. This review explores how AI can be applied to various aspects of vaccine development, such as predicting immune response from protein sequences, discovering adjuvants, optimizing vaccine doses, modeling vaccine supply chains, and predicting protein structures. We also address the challenges and ethical issues that emerge from the use of AI in vaccine development, such as data privacy, algorithmic bias, and health data sensitivity. We contend that AI has immense potential to accelerate vaccine development and respond to future pandemics, but it also requires careful attention to the quality and validity of the data and methods used.
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Affiliation(s)
| | | | | | | | | | - Deepanker Bisht
- Indian Veterinary Research Institute, Izatnagar, U.P., India
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Mathkor DM, Mathkor N, Bassfar Z, Bantun F, Slama P, Ahmad F, Haque S. Multirole of the internet of medical things (IoMT) in biomedical systems for managing smart healthcare systems: An overview of current and future innovative trends. J Infect Public Health 2024; 17:559-572. [PMID: 38367570 DOI: 10.1016/j.jiph.2024.01.013] [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: 04/06/2023] [Revised: 01/16/2024] [Accepted: 01/18/2024] [Indexed: 02/19/2024] Open
Abstract
Internet of Medical Things (IoMT) is an emerging subset of Internet of Things (IoT), often called as IoT in healthcare, refers to medical devices and applications with internet connectivity, is exponentially gaining researchers' attention due to its wide-ranging applicability in biomedical systems for Smart Healthcare systems. IoMT facilitates remote health biomedical system and plays a crucial role within the healthcare industry to enhance precision, reliability, consistency and productivity of electronic devices used for various healthcare purposes. It comprises a conceptualized architecture for providing information retrieval strategies to extract the data from patient records using sensors for biomedical analysis and diagnostics against manifold diseases to provide cost-effective medical solutions, quick hospital treatments, and personalized healthcare. This article provides a comprehensive overview of IoMT with special emphasis on its current and future trends used in biomedical systems, such as deep learning, machine learning, blockchains, artificial intelligence, radio frequency identification, and industry 5.0.
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Affiliation(s)
- Darin Mansor Mathkor
- Research and Scientific Studies Unit, Department of Nursing, College of Nursing and Health Sciences, Jazan University, Jazan 45142, Saudi Arabia
| | - Noof Mathkor
- Department of Pathology, Ministry of National Guard Health Affairs (MNGHA), Riyadh, Saudi Arabia
| | - Zaid Bassfar
- Department of Information Technology, Faculty of Computers and Information Technology, University of Tabuk, Tabuk, Saudi Arabia
| | - Farkad Bantun
- Department of Microbiology, Faculty of Medicine, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Petr Slama
- Laboratory of Animal Immunology and Biotechnology, Department of Animal Morphology, Physiology and Genetics, Mendel University in Brno, 61300 Brno, Czech Republic
| | - Faraz Ahmad
- Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore 632014, India
| | - Shafiul Haque
- Research and Scientific Studies Unit, Department of Nursing, College of Nursing and Health Sciences, Jazan University, Jazan 45142, Saudi Arabia; Gilbert and Rose-Marie Chagoury School of Medicine, Lebanese American University, Beirut, Lebanon; Centre of Medical and Bio-Allied Health Sciences Research, Ajman University, Ajman, United Arab Emirates.
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Hakmi M, Bouricha EM, Soussi A, Bzioui IA, Belyamani L, Ibrahimi A. Computational Drug Design Strategies for Fighting the COVID-19 Pandemic. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2024; 1457:199-214. [PMID: 39283428 DOI: 10.1007/978-3-031-61939-7_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/08/2024]
Abstract
The advent of COVID-19 has brought the use of computer tools to the fore in health research. In recent years, computational methods have proven to be highly effective in a variety of areas, including genomic surveillance, host range prediction, drug target identification, and vaccine development. They were also instrumental in identifying new antiviral compounds and repurposing existing therapeutics to treat COVID-19. Using computational approaches, researchers have made significant advances in understanding the molecular mechanisms of COVID-19 and have developed several promising drug candidates and vaccines. This chapter highlights the critical importance of computational drug design strategies in elucidating various aspects of COVID-19 and their contribution to advancing global drug design efforts during the pandemic. Ultimately, the use of computing tools will continue to play an essential role in health research, enabling researchers to develop innovative solutions to combat new and emerging diseases.
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Affiliation(s)
- Mohammed Hakmi
- Medical Biotechnology Laboratory (MedBiotech), Faculty of Medicine and Pharmacy, Bioinova Research Center, Mohammed Vth University, Rabat, Morocco.
- Mohammed VI Center for Research and Innovation (CM6), Rabat, Morocco.
| | - El Mehdi Bouricha
- Medical Biotechnology Laboratory (MedBiotech), Faculty of Medicine and Pharmacy, Bioinova Research Center, Mohammed Vth University, Rabat, Morocco
- Mohammed VI Center for Research and Innovation (CM6), Rabat, Morocco
| | - Abdellatif Soussi
- Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, 16145, Genova, Italy
| | - Ilias Abdeslam Bzioui
- Department of Gynecology and Obstetrics, Faculty of Medicine, Abdelmalek Essaâdi University Hospital, Tangier, Morocco
| | - Lahcen Belyamani
- Mohammed VI Center for Research and Innovation (CM6), Rabat, Morocco
- Mohammed VI University of Health Sciences (UM6SS), Casablanca, Morocco
- Emergency Department, Medical and Pharmacy School, Military Hospital Mohammed V, Mohammed V University, Rabat, Morocco
| | - Azeddine Ibrahimi
- Medical Biotechnology Laboratory (MedBiotech), Faculty of Medicine and Pharmacy, Bioinova Research Center, Mohammed Vth University, Rabat, Morocco
- Mohammed VI Center for Research and Innovation (CM6), Rabat, Morocco
- Mohammed VI University of Health Sciences (UM6SS), Casablanca, Morocco
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Guo S, Zhang D, Wang H, An Q, Yu G, Han J, Jiang C, Huang J. Editorial: Computational and systematic analysis of multi-omics data for drug discovery and development. Front Med (Lausanne) 2023; 10:1146896. [PMID: 36895719 PMCID: PMC9989304 DOI: 10.3389/fmed.2023.1146896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 01/30/2023] [Indexed: 02/23/2023] Open
Affiliation(s)
- Shicheng Guo
- Department of Medical Genetics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
| | - Dake Zhang
- Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Engineering Medicine, Beihang University, Beijing, China
| | - Hu Wang
- Institute of Cell Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Qin An
- Salk Institute for Biological Studies, La Jolla, CA, United States
| | - Guangchuang Yu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Junwei Han
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Chunjie Jiang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jianfeng Huang
- Salk Institute for Biological Studies, La Jolla, CA, United States
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Cheng L. Omics data analysis and integration for COVID-19 patients - editorial. Brief Bioinform 2022; 23:bbac471. [PMID: 36274237 DOI: 10.1093/bib/bbac471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Affiliation(s)
- Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
- NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, Heilongjiang 150028, China
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Zhang Y, Luo M, Wu P, Wu S, Lee TY, Bai C. Application of Computational Biology and Artificial Intelligence in Drug Design. Int J Mol Sci 2022; 23:13568. [PMID: 36362355 PMCID: PMC9658956 DOI: 10.3390/ijms232113568] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 10/29/2022] [Accepted: 11/03/2022] [Indexed: 08/24/2023] Open
Abstract
Traditional drug design requires a great amount of research time and developmental expense. Booming computational approaches, including computational biology, computer-aided drug design, and artificial intelligence, have the potential to expedite the efficiency of drug discovery by minimizing the time and financial cost. In recent years, computational approaches are being widely used to improve the efficacy and effectiveness of drug discovery and pipeline, leading to the approval of plenty of new drugs for marketing. The present review emphasizes on the applications of these indispensable computational approaches in aiding target identification, lead discovery, and lead optimization. Some challenges of using these approaches for drug design are also discussed. Moreover, we propose a methodology for integrating various computational techniques into new drug discovery and design.
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Affiliation(s)
- Yue Zhang
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
| | - Mengqi Luo
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Peng Wu
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518055, China
| | - Song Wu
- South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Tzong-Yi Lee
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
| | - Chen Bai
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
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Sharma PP, Bansal M, Sethi A, Poonam, Pena L, Goel VK, Grishina M, Chaturvedi S, Kumar D, Rathi B. Computational methods directed towards drug repurposing for COVID-19: advantages and limitations. RSC Adv 2021; 11:36181-36198. [PMID: 35492747 PMCID: PMC9043418 DOI: 10.1039/d1ra05320e] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Accepted: 10/07/2021] [Indexed: 12/19/2022] Open
Abstract
Novel coronavirus disease 2019 (COVID-19) has significantly altered the socio-economic status of countries. Although vaccines are now available against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a causative agent for COVID-19, it continues to transmit and newer variants of concern have been consistently emerging world-wide. Computational strategies involving drug repurposing offer a viable opportunity to choose a medication from a rundown of affirmed drugs against distinct diseases including COVID-19. While pandemics impede the healthcare systems, drug repurposing or repositioning represents a hopeful approach in which existing drugs can be remodeled and employed to treat newer diseases. In this review, we summarize the diverse computational approaches attempted for developing drugs through drug repurposing or repositioning against COVID-19 and discuss their advantages and limitations. To this end, we have outlined studies that utilized computational techniques such as molecular docking, molecular dynamic simulation, disease-disease association, drug-drug interaction, integrated biological network, artificial intelligence, machine learning and network medicine to accelerate creation of smart and safe drugs against COVID-19.
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Affiliation(s)
- Prem Prakash Sharma
- Laboratory For Translational Chemistry and Drug Discovery, Department of Chemistry, Hansraj College, University of Delhi Delhi 110007 India
| | - Meenakshi Bansal
- Laboratory For Translational Chemistry and Drug Discovery, Department of Chemistry, Hansraj College, University of Delhi Delhi 110007 India
| | - Aaftaab Sethi
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research (NIPER) Hyderabad India
| | - Poonam
- Department of Chemistry, Miranda House, University of Delhi Delhi 110007 India
| | - Lindomar Pena
- Department of Virology, Aggeu Magalhaes, Institute (IAM), Oswaldo Cruz Foundation (Fiocruz) Recife 50670-420 Pernambuco Brazil
| | - Vijay Kumar Goel
- School of Physical Sciences, Jawaharlal Nehru University New Delhi 110067 India
| | - Maria Grishina
- South Ural State University, Laboratory of Computational Modelling of Drugs Pr. Lenina 76 454080 Russia
| | - Shubhra Chaturvedi
- Division of Cyclotron and Radiopharmaceutical Sciences, Institute of Nuclear Medicine and Allied Sciences New Delhi 110054 India
| | - Dhruv Kumar
- Amity Institute of Molecular Medicine & Stem Cell Research (AIMMSCR), Amity University Uttar Pradesh Noida 201313 India
| | - Brijesh Rathi
- Laboratory For Translational Chemistry and Drug Discovery, Department of Chemistry, Hansraj College, University of Delhi Delhi 110007 India
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