Deep learning in next-generation vaccine development for infectious diseases
The landscape of vaccine development was changed in the genomic era with the help of computer science. Computer-aided vaccine epitope selection has become a foundation of rational vaccine design. Similarly, artificial intelligence (AI) is quickly transforming the vaccine development landscape. Deep...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Molecular Therapy: Nucleic Acids |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2162253125001404 |
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| author | Manojit Bhattacharya Yi-Hao Lo Srijan Chatterjee Arpita Das Zhi-Hong Wen Chiranjib Chakraborty |
| author_facet | Manojit Bhattacharya Yi-Hao Lo Srijan Chatterjee Arpita Das Zhi-Hong Wen Chiranjib Chakraborty |
| author_sort | Manojit Bhattacharya |
| collection | DOAJ |
| description | The landscape of vaccine development was changed in the genomic era with the help of computer science. Computer-aided vaccine epitope selection has become a foundation of rational vaccine design. Similarly, artificial intelligence (AI) is quickly transforming the vaccine development landscape. Deep learning (DL), a subset of AI, is used in the landscape of vaccine development in terms of its algorithms, tools, and technologies. This review article discussed the developmental history of the modern era of vaccine development strategies using both immunoinformatics with DL models, identification strategies of T cell epitopes and B cell epitopes through immunoinformatics and DL models, vaccine constructs development strategies using linker and adjuvant, and characterization strategies of vaccine construct using bioinformatics and immunoinformatics. Similarly, the article discusses different tools and technologies, from epitope mapping and vaccine construct development to characterization. Again, it also highlighted recent paradigm shifts, DL-based strategies in vaccine development, and different DL-based tools used for epitope mapping and vaccine construct development. However, integrated frameworks connecting the bioinformatics and DL approaches are rapidly progressing, which are necessary for DL-assisted epitope prediction and the subsequent steps for vaccine development. DL-assisted vaccine development is rapid and cost-effective, changing the scenario of next-generation vaccine development very fast. |
| format | Article |
| id | doaj-art-ecd7e72a27e24b2480f148f16659aa8e |
| institution | Kabale University |
| issn | 2162-2531 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Molecular Therapy: Nucleic Acids |
| spelling | doaj-art-ecd7e72a27e24b2480f148f16659aa8e2025-08-20T03:24:08ZengElsevierMolecular Therapy: Nucleic Acids2162-25312025-09-0136310258610.1016/j.omtn.2025.102586Deep learning in next-generation vaccine development for infectious diseasesManojit Bhattacharya0Yi-Hao Lo1Srijan Chatterjee2Arpita Das3Zhi-Hong Wen4Chiranjib Chakraborty5Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore, Odisha 756020, IndiaDepartment of Family Medicine, Zuoying Armed Forces General Hospital, Kaohsiung 81342, Taiwan; Department of Nursing, Meiho University, Pingtung County 91200, TaiwanInstitute for Skeletal Aging & Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon, Gangwon-Do 24252, Republic of KoreaDepartment of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal 700126, IndiaDepartment of Marine Biotechnology and Resources, National Sun Yat-Sen University, #70 Lien-Hai Road, Kaohsiung 804201, Taiwan; National Museum of Marine Biology & Aquarium, # 2 Houwan Road, Checheng, Pingtung 94450, TaiwanDepartment of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal 700126, India; Corresponding author: Chiranjib Chakraborty, Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal 700126, India.The landscape of vaccine development was changed in the genomic era with the help of computer science. Computer-aided vaccine epitope selection has become a foundation of rational vaccine design. Similarly, artificial intelligence (AI) is quickly transforming the vaccine development landscape. Deep learning (DL), a subset of AI, is used in the landscape of vaccine development in terms of its algorithms, tools, and technologies. This review article discussed the developmental history of the modern era of vaccine development strategies using both immunoinformatics with DL models, identification strategies of T cell epitopes and B cell epitopes through immunoinformatics and DL models, vaccine constructs development strategies using linker and adjuvant, and characterization strategies of vaccine construct using bioinformatics and immunoinformatics. Similarly, the article discusses different tools and technologies, from epitope mapping and vaccine construct development to characterization. Again, it also highlighted recent paradigm shifts, DL-based strategies in vaccine development, and different DL-based tools used for epitope mapping and vaccine construct development. However, integrated frameworks connecting the bioinformatics and DL approaches are rapidly progressing, which are necessary for DL-assisted epitope prediction and the subsequent steps for vaccine development. DL-assisted vaccine development is rapid and cost-effective, changing the scenario of next-generation vaccine development very fast.http://www.sciencedirect.com/science/article/pii/S2162253125001404MT: Bioinformaticsdeep learningnext-generation vaccineinfectious diseasesimmunoinformatics |
| spellingShingle | Manojit Bhattacharya Yi-Hao Lo Srijan Chatterjee Arpita Das Zhi-Hong Wen Chiranjib Chakraborty Deep learning in next-generation vaccine development for infectious diseases Molecular Therapy: Nucleic Acids MT: Bioinformatics deep learning next-generation vaccine infectious diseases immunoinformatics |
| title | Deep learning in next-generation vaccine development for infectious diseases |
| title_full | Deep learning in next-generation vaccine development for infectious diseases |
| title_fullStr | Deep learning in next-generation vaccine development for infectious diseases |
| title_full_unstemmed | Deep learning in next-generation vaccine development for infectious diseases |
| title_short | Deep learning in next-generation vaccine development for infectious diseases |
| title_sort | deep learning in next generation vaccine development for infectious diseases |
| topic | MT: Bioinformatics deep learning next-generation vaccine infectious diseases immunoinformatics |
| url | http://www.sciencedirect.com/science/article/pii/S2162253125001404 |
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