Artificial intelligence (AI) accelerates vaccine design for protein subunit, viral vector and mRNA platforms.
AI revolutionizes vaccine development by modeling immune response and accelerating clinical trials.
AI-driven strategies may enhance the efficacy of next-generation vaccines, improving pandemic preparedness.
| [1] | Watson O. J., Barnsley G., Toor J., et al. (2022). Global impact of the first year of COVID-19 vaccination: A mathematical modelling study. Lancet Infect. Dis. 22:1293−1302. DOI:10.1016/S1473-3099(22)00320-6 |
| [2] | Minor P. D. (2015). Live attenuated vaccines: Historical successes and current challenges. Virology 479-480:379-392. DOI: 10.1016/j.virol.2015.03.032. |
| [3] | Hill H. A., Elam-Evans L. D., Yankey D., et al. (2018). Vaccination coverage among children aged 19-35 months - United States, 2017. MMWR. Morbidity and mortality weekly report. 67:1123−1128. DOI:10.15585/mmwr.mm6740a4 |
| [4] | Organization W. H. (2024). Global immunization efforts have saved at least 154 million lives over the past 50 years. https://www.who.int/news/item/24-04-2024-global-immunization-efforts-have-saved-at-least-154-million-lives-over-the-past-50-years |
| [5] | Krishnamoorthy Y., Sakthivel M., Eliyas S. K., et al. (2019). Worldwide trend in measles incidence from 1980 to 2016: A pooled analysis of evidence from 194 WHO Member States. J Postgrad. Med. 65:160−163. DOI:10.4103/jpgm.JPGM_508_18 |
| [6] | Jin L., Li Z., Zhang X., et al. (2022). CoronaVac: A review of efficacy, safety, and immunogenicity of the inactivated vaccine against SARS-CoV-2. Hum. Vaccin. Immunother. 18:2096970. DOI:10.1080/21645515.2022.2096970 |
| [7] | Hu L., Sun J., Wang Y., et al. (2023). A review of inactivated COVID-19 vaccine development in China: Focusing on safety and efficacy in special populations. Vaccines (Basel) 11:1045. DOI: 10.3390/vaccines11061045 |
| [8] | Heidary M., Kaviar V. H., Shirani M., et al. (2022). A comprehensive review of the protein subunit vaccines against COVID-19. Front. Microbiol. 13:927306. DOI:10.3389/fmicb.2022.927306 |
| [9] | Richmond P., Hatchuel L., Dong M., et al. (2021). Safety and immunogenicity of S-Trimer (SCB-2019), a protein subunit vaccine candidate for COVID-19 in healthy adults: A phase 1, randomised, double-blind, placebo-controlled trial. Lancet. 397:682−694. DOI:10.1016/S0140-6736(21)00241-5 |
| [10] | Travieso T., Li J., Mahesh S., et al. (2022). The use of viral vectors in vaccine development. NPJ Vaccines. 7:75. DOI:10.1038/s41541-022-00503-y |
| [11] | Vanaparthy R., Mohan G., Vasireddy D., et al. (2021). Review of COVID-19 viral vector-based vaccines and COVID-19 variants. Infez. Med. 29:328−338. DOI:10.53854/liim-2903-3 |
| [12] | Draper S. J. and Heeney J. L. (2010). Viruses as vaccine vectors for infectious diseases and cancer. Nat. Rev. Microbiol. 8:62−73. DOI:10.1038/nrmicro2240 |
| [13] | Wang S., Liang B., Wang W., et al. (2023). Viral vectored vaccines: Design, development, preventive and therapeutic applications in human diseases. Signal Transduct. Target. Ther. 8:149. DOI:10.1038/s41392-023-01408-5 |
| [14] | Folegatti P. M., Ewer K. J., Aley P. K., et al. (2020). Safety and immunogenicity of the ChAdOx1 nCoV-19 vaccine against SARS-CoV-2: A preliminary report of a phase 1/2, single-blind, randomised controlled trial. Lancet. 396:467−478. DOI:10.1016/S0140-6736(20)31604-4 |
| [15] | Sadoff J., Le Gars M., Shukarev G., et al. (2021). Interim results of a phase 1-2a trial of Ad26.COV2.S Covid-19 vaccine. N. Engl. J. Med. 384:1824-1835. DOI: 10.1056/NEJMoa2034201. |
| [16] | Zhu F. C., Li Y. H., Guan X. H., et al. (2020). Safety, tolerability, and immunogenicity of a recombinant adenovirus type-5 vectored COVID-19 vaccine: A dose-escalation, open-label, non-randomised, first-in-human trial. Lancet. 395:1845−1854. DOI:10.1016/S0140-6736(20)31208-3 |
| [17] | Li J. X., Hou L. H., Gou J. B., et al. (2023). Safety, immunogenicity and protection of heterologous boost with an aerosolised Ad5-nCoV after two-dose inactivated COVID-19 vaccines in adults: A multicentre, open-label phase 3 trial. Lancet Infect. Dis. 23:1143−1152. DOI:10.1016/S1473-3099(23)00350-X |
| [18] | Wang S. Y., Liu W. Q., Li Y. Q., et al. (2023). A China-developed adenovirus vector-based COVID-19 vaccine: Review of the development and application of Ad5-nCov. Expert. Rev. Vaccines. 22:704−713. DOI:10.1080/14760584.2023.2242528 |
| [19] | Qin F., Xia F., Chen H., et al. (2021). A guide to nucleic acid vaccines in the prevention and treatment of infectious diseases and cancers: From basic principles to current applications. Front. Cell Dev. Biol. 9:633776. DOI:10.3389/fcell.2021.633776 |
| [20] | Pardi N., Hogan M. J., Porter F. W., et al. (2018). mRNA vaccines - A new era in vaccinology. Nat. Rev. Drug Discov. 17:261−279. DOI:10.1038/nrd.2017.243 |
| [21] | Chaudhary N., Weissman D. and Whitehead K. A. (2021). mRNA vaccines for infectious diseases: Principles, delivery and clinical translation. Nat. Rev. Drug Discov. 20:817−838. DOI:10.1038/s41573-021-00283-5 |
| [22] | Ghosh A., Larrondo-Petrie M. M. and Pavlovic M. (2023). Revolutionizing vaccine development for COVID-19: A review of AI-based approaches. Information 14:665. DOI:10.3390/info14120665 |
| [23] | McCarthy J. (2007). What is artificial intelligence. Stanford University. http://www-formal.stanford.edu/jmc/ |
| [24] | Krenn M., Pollice R., Guo S. Y., et al. (2022). On scientific understanding with artificial intelligence. Nat. Rev. Phys. 4:761−769. DOI:10.1038/s42254-022-00518-3 |
| [25] | Shinde P. P. and Shah S. (2018). A review of machine learning and deep learning applications. 2018 Fourth international conference on computing communication control and automation (ICCUBEA). DOI:10.1109/ICCUBEA.2018.8697857 |
| [26] | Rajoub B. (2020). Supervised and unsupervised learning. Zgallai, W. (ed). In Biomedical Signal Processing and Artificial Intelligence in Healthcare. (Academic Press), pp: 51-89. DOI: 10.1016/B978-0-12-818946-7.00003-2 |
| [27] | Bravi B. (2024). Development and use of machine learning algorithms in vaccine target selection. NPJ Vaccines 9:15. DOI:10.1038/s41541-023-00795-8 |
| [28] | Hederman A. P. and Ackerman M. E. (2023). Leveraging deep learning to improve vaccine design. Trends Immunol. 44:333−344. DOI:10.1016/j.it.2023.03.002 |
| [29] | Senior A. W., Evans R., Jumper J., et al. (2020). Improved protein structure prediction using potentials from deep learning. Nature 577:706−710. DOI:10.1038/s41586-019-1923-7 |
| [30] | Du Z., Su H., Wang W., et al. (2021). The trRosetta server for fast and accurate protein structure prediction. Nat. Protoc. 16:5634−5651. DOI:10.1038/s41596-021-00628-9 |
| [31] | Baek M., DiMaio F., Anishchenko I., et al. (2021). Accurate prediction of protein structures and interactions using a three-track neural network. Science. 373:871−876. DOI:10.1126/science.abj8754 |
| [32] | He H., He B., Guan L., et al. (2024). De novo generation of SARS-CoV-2 antibody CDRH3 with a pre-trained generative large language model. Nat. Commun. 15:6867. DOI:10.1038/s41467-024-50903-y |
| [33] | Chakraborty C., Bhattacharya M. and Lee S. S. (2023). Artificial intelligence enabled ChatGPT and large language models in drug target discovery, drug discovery, and development. Mol. Ther. Nucleic Acids 33:866−868. DOI:10.1016/j.omtn.2023.08.009 |
| [34] | Madani A., Krause B., Greene E. R., et al. (2023). Large language models generate functional protein sequences across diverse families. Nat. Biotechnol. 41:1099−1106. DOI:10.1038/s41587-022-01618-2 |
| [35] | Jablonka K. M., Schwaller P., Ortega-Guerrero A., et al. (2024). Leveraging large language models for predictive chemistry. Nat. Mach. Intell. 6:161−169. DOI:10.1038/s42256-023-00788-1 |
| [36] | Khakzad H., Igashov I., Schneuing A., et al. (2023). A new age in protein design empowered by deep learning. Cell Syst. 14:925−939. DOI:10.1016/j.cels.2023.10.006 |
| [37] | Peng C., Yang X., Chen A., et al. (2023). A study of generative large language model for medical research and healthcare. NPJ Digit. Med. 6:210. DOI:10.1038/s41746-023-00958-w |
| [38] | Chu A. E., Lu T. and Huang P. S. (2024). Sparks of function by de novo protein design. Nat. Biotechnol. 42:203−215. DOI:10.1038/s41587-024-02133-2 |
| [39] | Awad M. and Khanna R. (2015). Efficient learning machines. Pepper J., Weiss S. and Hauke P. (eds). Theories, concepts, and applications for engineers and system designers (Apress), pp: 1-18. DOI:10.1007/978-1-4302-5990-9. |
| [40] | Lutz I. D., Wang S., Norn C., et al. (2023). Top-down design of protein architectures with reinforcement learning. Science 380:266−273. DOI:10.1126/science.adf6591 |
| [41] | Correia B. E., Ban Y. E., Holmes M. A., et al. (2010). Computational design of epitope-scaffolds allows induction of antibodies specific for a poorly immunogenic HIV vaccine epitope. Structure 18:1116−1126. DOI:10.1016/j.str.2010.06.010 |
| [42] | Gm H., Gourisaria M. K., Pandey M., et al. (2020). A comprehensive survey and analysis of generative models in machine learning. Computer Science Review. 38:100285. DOI:10.1016/j.cosrev.2020.100285 |
| [43] | Lyu S., Sowlati-Hashjin S. and Garton M. (2024). Variational autoencoder for design of synthetic viral vector serotypes. Nat. Mach. Intell. 6:147−160. DOI:10.1038/s42256-023-00787-2 |
| [44] | Watson J. L., Juergens D., Bennett N. R., et al. (2023). De novo design of protein structure and function with RFdiffusion. Nature 620:1089−1100. DOI:10.1038/s41586-023-06415-8 |
| [45] | Abramson J., Adler J., Dunger J., et al. (2024). Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630:493−500. DOI:10.1038/s41586-024-07487-w |
| [46] | Jumper J., Evans R., Pritzel A., et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature 596:583−589. DOI:10.1038/s41586-021-03819-2 |
| [47] | Thirunavukarasu A. J., Ting D. S. J., Elangovan K., et al. (2023). Large language models in medicine. Nat. Med. 29:1930−1940. DOI:10.1038/s41591-023-02448-8 |
| [48] | Ferruz N., Schmidt S. and Höcker B. (2022). ProtGPT2 is a deep unsupervised language model for protein design. Nat. Commun. 13:4348. DOI:10.1038/s41467-022-32007-7 |
| [49] | Guntuboina C., Das A., Mollaei P., et al. (2023). PeptideBERT: A language model based on transformers for peptide property prediction. J. Phys. Chem. Lett. 14:10427−10434. DOI:10.1021/acs.jpclett.3c02398 |
| [50] | Lin Z., Akin H., Rao R., et al. (2023). Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379:1123−1130. DOI:10.1126/science.ade2574 |
| [51] | Chu Y., Yu D., Li Y., et al. (2024). A 5' UTR language model for decoding untranslated regions of mRNA and function predictions. Nat. Mach. Intell. 6:449−460. DOI:10.1038/s42256-024-00823-9 |
| [52] | Chen B., Cheng X., Li P., et al. (2025). xTrimoPGLM: Unified 100-billion-parameter pretrained transformer for deciphering the language of proteins. Nat. Methods. 22:1028−1039. DOI:10.1038/s41592-025-02636-z |
| [53] | Brixi G., Durrant M. G., Ku J., et al. (2025). Genome modeling and design across all domains of life with Evo 2. bioRxiv.2025.02.18.638918. DOI: 10.1101/2025.02.18.638918 |
| [54] | Nguyen E., Poli M., Durrant M. G., et al. (2024). Sequence modeling and design from molecular to genome scale with Evo. Science 386:eado9336. DOI:10.1126/science.ado9336 |
| [55] | Singhal K., Azizi S., Tu T., et al. (2023). Large language models encode clinical knowledge. Nature 620:172−180. DOI:10.1038/s41586-023-06291-2 |
| [56] | Zheng S., Li Y., Chen S., et al. (2020). Predicting drug–protein interaction using quasi-visual questionanswering system. Nat. Mach. Intell. 2:134−140. DOI:10.1038/s42256-020-0152-y |
| [57] | Malone B., Simovski B., Moline C., et al. (2020). Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2 leading to universal blueprints for vaccine designs. Sci. Rep. 10:22375. DOI:10.1038/s41598-020-78758-5 |
| [58] | Kanampalliwar A. M. (2020). Reverse vaccinology and its applications. Methods Mol. Biol. 2131:1−16. DOI:10.1007/978-1-0716-0389-5_1 |
| [59] | Santos-Júnior C. D., Torres M. D. T., Duan Y., et al. (2024). Discovery of antimicrobial peptides in the global microbiome with machine learning. Cell 187:3761−3778.e3716. DOI:10.1016/j.cell.2024.05.013 |
| [60] | Correia B. E., Bates J. T., Loomis R. J., et al. (2014). Proof of principle for epitope-focused vaccine design. Nature 507:201−206. DOI:10.1038/nature12966 |
| [61] | Castro K. M., Watson J. L., Wang J., et al. (2024). Accurate single domain scaffolding of three non-overlapping protein epitopes using deep learning. bioRxiv.2024.05.07.592871. DOI: 10.1101/2024.05.07.592871 |
| [62] | Fu D., Wang W., Zhang Y., et al. (2024). Self-assembling nanoparticle engineered from the ferritinophagy complex as a rabies virus vaccine candidate. Nat. Commun. 15:8601. DOI:10.1038/s41467-024-52908-z |
| [63] | Song J. Y., Choi W. S., Heo J. Y., et al. (2022). Safety and immunogenicity of a SARS-CoV-2 recombinant protein nanoparticle vaccine (GBP510) adjuvanted with AS03: A randomised, placebo-controlled, observer-blinded phase 1/2 trial. EClinicalMedicine. 51:101569. DOI:10.1016/j.eclinm.2022.101569 |
| [64] | Song J. Y., Choi W. S., Heo J. Y., et al. (2023). Immunogenicity and safety of SARS-CoV-2 recombinant protein nanoparticle vaccine GBP510 adjuvanted with AS03: Interim results of a randomised, active-controlled, observer-blinded, phase 3 trial. EClinicalMedicine 64:102140. DOI:10.1016/j.eclinm.2023.102140 |
| [65] | Brouwer P. J. M., Antanasijevic A., Berndsen Z., et al. (2019). Enhancing and shaping the immunogenicity of native-like HIV-1 envelope trimers with a two-component protein nanoparticle. Nat. Commun. 10:4272. DOI:10.1038/s41467-019-12080-1 |
| [66] | Walls A. C., Fiala B., Schafer A., et al. (2020). Elicitation of potent neutralizing antibody responses by designed protein nanoparticle vaccines for SARS-CoV-2. Cell 183:1367-1382 e1317. DOI: 10.1016/j.cell.2020.10.043 |
| [67] | Jacob-Dolan C., Yu J., McMahan K., et al. (2023). Immunogenicity and protective efficacy of GBP510/AS03 vaccine against SARS-CoV-2 delta challenge in rhesus macaques. NPJ Vaccines 8:23. DOI:10.1038/s41541-023-00622-0 |
| [68] | Ueda G., Antanasijevic A., Fallas J. A., et al. (2020). Tailored design of protein nanoparticle scaffolds for multivalent presentation of viral glycoprotein antigens. Elife 9:e57659. DOI: 10.7554/eLife.57659 |
| [69] | Khattak M. A. A., Ascierto P. A., Queirolo P., et al. (2024). 1084P Phase II study of AI-designed personalized neoantigen cancer vaccine, EVX-01, in combination with pembrolizumab in advanced melanoma. Ann. Oncol. 35:S718−S719. DOI:10.1016/j.annonc.2024.08.1152 |
| [70] | Lisanza S. L., Gershon J. M., Tipps S. W. K., et al. (2024). Multistate and functional protein design using RoseTTAFold sequence space diffusion. Nat. Biotechnol. DOI: 10.1038/s41587-024-02395-w |
| [71] | Liang Y., Shao S., Li X. Y., et al. (2024). Mutating a flexible region of the RSV F protein can stabilize the prefusion conformation. Science 385:1484−1491. DOI:10.1126/science.adp2362 |
| [72] | Che Y., Gribenko A. V., Song X., et al. (2023). Rational design of a highly immunogenic prefusion-stabilized F glycoprotein antigen for a respiratory syncytial virus vaccine. Sci. Transl. Med. 15:eade6422. DOI:10.1126/scitranslmed.ade6422 |
| [73] | Bakkers M. J. G., Cox F., Koornneef A., et al. (2024). A foldon-free prefusion F trimer vaccine for respiratory syncytial virus to reduce off-target immune responses. Nat. Microbiol. 9:3254−3267. DOI:10.1038/s41564-024-01860-1 |
| [74] | Bakkers M. J. G., Ritschel T., Tiemessen M., et al. (2024). Efficacious human metapneumovirus vaccine based on AI-guided engineering of a closed prefusion trimer. Nat. Commun. 15:6270. DOI:10.1038/s41467-024-50659-5 |
| [75] | Williams J. A., Biancucci M., Lessen L., et al. (2023). Structural and computational design of a SARS-CoV-2 spike antigen with improved expression and immunogenicity. Sci. Adv. 9:eadg0330. DOI:10.1126/sciadv.adg0330 |
| [76] | Zabaleta N., Dai W., Bhatt U., et al. (2021). An AAV-based, room-temperature-stable, single-dose COVID-19 vaccine provides durable immunogenicity and protection in non-human primates. Cell Host Microbe 29:1437-1453 e1438. DOI: 10.1016/j.chom.2021.08.002. |
| [77] | Zhao S., Ke J., Yang B., et al. (2022). A protective AAV vaccine for SARS-CoV-2. Signal Transduct. Target. Ther. 7:310. DOI:10.1038/s41392-022-01158-w |
| [78] | Ploquin A., Szecsi J., Mathieu C., et al. (2013). Protection against henipavirus infection by use of recombinant adeno-associated virus-vector vaccines. J. Infect. Dis. 207:469−478. DOI:10.1093/infdis/jis699 |
| [79] | Smalley E. (2017). First AAV gene therapy poised for landmark approval. Nat. Biotechnol. 35:998−999. DOI:10.1038/nbt1117-998 |
| [80] | Russell S., Bennett J., Wellman J. A., et al. (2017). Efficacy and safety of voretigene neparvovec (AAV2-hRPE65v2) in patients with RPE65-mediated inherited retinal dystrophy: a randomised, controlled, open-label, phase 3 trial. Lancet. 390:849−860. DOI:10.1016/S0140-6736(17)31868-8 |
| [81] | Grimm D., Lee J. S., Wang L., et al. (2008). In vitro and in vivo gene therapy vector evolution via multispecies interbreeding and retargeting of adeno-associated viruses. J. Virol. 82:5887−5911. DOI:10.1128/JVI.00254-08 |
| [82] | Dalkara D., Byrne L. C., Klimczak R. R., et al. (2013). In vivo-directed evolution of a new adeno-associated virus for therapeutic outer retinal gene delivery from the vitreous. Sci. Transl. Med. 5:189ra176. DOI:10.1126/scitranslmed.3005708 |
| [83] | Tse L. V., Klinc K. A., Madigan V. J., et al. (2017). Structure-guided evolution of antigenically distinct adeno-associated virus variants for immune evasion. Proc. Natl. Acad. Sci. USA 114:E4812−E4821. DOI:10.1073/pnas.1704766114 |
| [84] | Bryant D. H., Bashir A., Sinai S., et al. (2021). Deep diversification of an AAV capsid protein by machine learning. Nat. Biotechnol. 39:691−696. DOI:10.1038/s41587-020-00793-4 |
| [85] | Mendonca S. A., Lorincz R., Boucher P., et al. (2021). Adenoviral vector vaccine platforms in the SARS-CoV-2 pandemic. NPJ Vaccines. 6:97. DOI:10.1038/s41541-021-00356-x |
| [86] | Fausther-Bovendo H. and Kobinger G. P. (2014). Pre-existing immunity against Ad vectors: humoral, cellular, and innate response, what's important. Hum. Vaccin. Immunother. 10:2875−2884. DOI:10.4161/hv.29594 |
| [87] | Rux J. J., Kuser P. R. and Burnett R. M. (2003). Structural and phylogenetic analysis of adenovirus hexons by use of high-resolution x-ray crystallographic, molecular modeling, and sequence-based methods. J. Virol. 77:9553−9566. DOI:10.1128/jvi.77.17.9553-9566.2003 |
| [88] | Flickinger J. C., Jr., Singh J., Carlson R., et al. (2020). Chimeric Ad5.F35 vector evades anti-adenovirus serotype 5 neutralization opposing GUCY2C-targeted antitumor immunity. J. Immunother. Cancer 8. DOI: 10.1136/jitc-2020-001046 |
| [89] | Zhu D., Brookes D. H., Busia A., et al. (2024). Optimal trade-off control in machine learning-based library design, with application to adeno-associated virus (AAV) for gene therapy. Sci. Adv. 10:eadj3786. DOI:10.1126/sciadv.adj3786 |
| [90] | Marques A. D., Kummer M., Kondratov O., et al. (2021). Applying machine learning to predict viral assembly for adeno-associated virus capsid libraries. Mol. Ther. Methods Clin. Dev. 20:276−286. DOI:10.1016/j.omtm.2020.11.017 |
| [91] | Lee J., Woodruff M. C., Kim E. H., et al. (2023). Knife's edge: Balancing immunogenicity and reactogenicity in mRNA vaccines. Exp. Mol. Med. 55:1305−1313. DOI:10.1038/s12276-023-00999-x |
| [92] | Petsch B., Schnee M., Vogel A. B., et al. (2012). Protective efficacy of in vitro synthesized, specific mRNA vaccines against influenza A virus infection. Nat. Biotechnol. 30:1210−1216. DOI:10.1038/nbt.2436 |
| [93] | Polack F. P., Thomas S. J., Kitchin N., et al. (2020). Safety and efficacy of the BNT162b2 mRNA Covid-19 vaccine. N. Engl. J. Med. 383:2603−2615. DOI:10.1056/NEJMoa2034577 |
| [94] | Baden L. R., El Sahly H. M., Essink B., et al. (2021). Efficacy and safety of the mRNA-1273 SARS-CoV-2 vaccine. N. Engl. J. Med. 384:403−416. DOI:10.1056/NEJMoa2035389 |
| [95] | Park J. W., Lagniton P. N. P., Liu Y., et al. (2021). mRNA vaccines for COVID-19: What, why and how. Int. J Biol. Sci. 17:1446−1460. DOI:10.7150/ijbs.59233 |
| [96] | Paremskaia A. I., Kogan A. A., Murashkina A., et al. (2024). Codon-optimization in gene therapy: Promises, prospects and challenges. Front. Bioeng. Biotechnol. 12:1371596. DOI:10.3389/fbioe.2024.1371596 |
| [97] | Leppek K., Byeon G. W., Kladwang W., et al. (2022). Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics. Nat. Commun. 13:1536. DOI:10.1038/s41467-022-28776-w |
| [98] | Zhang H., Zhang L., Lin A., et al. (2023). Algorithm for optimized mRNA design improves stability and immunogenicity. Nature 621:396−403. DOI:10.1038/s41586-023-06127-z |
| [99] | Zheng W., Fong J. H. C., Wan Y. K., et al. (2023). Discovery of regulatory motifs in 5' untranslated regions using interpretable multi-task learning models. Cell Syst. 14:1103-1112 e1106. DOI: 10.1016/j.cels.2023.10.011 |
| [100] | Hinnebusch A. G., Ivanov I. P. and Sonenberg N. (2016). Translational control by 5'-untranslated regions of eukaryotic mRNAs. Science 352:1413−1416. DOI:10.1126/science.aad9868 |
| [101] | Cao J., Novoa E. M., Zhang Z., et al. (2021). High-throughput 5' UTR engineering for enhanced protein production in non-viral gene therapies. Nat. Commun. 12:4138. DOI:10.1038/s41467-021-24436-7 |
| [102] | Yuan Y., Wu Y., Cheng J., et al. (2024). Applications of artificial intelligence to lipid nanoparticle delivery. Particuology 90:88−97. DOI:10.1016/j.partic.2023.11.014 |
| [103] | Alameh M. G., Tombacz I., Bettini E., et al. (2022). Lipid nanoparticles enhance the efficacy of mRNA and protein subunit vaccines by inducing robust T follicular helper cell and humoral responses. Immunity 55:1136−1138. DOI:10.1016/j.immuni.2022.05.007 |
| [104] | Xu Y., Ma S., Cui H., et al. (2024). AGILE platform: A deep learning powered approach to accelerate LNP development for mRNA delivery. Nat. Commun. 15:6305. DOI:10.1038/s41467-024-50619-z |
| [105] | Witten J., Raji I., Manan R. S., et al. (2024). Artificial intelligence-guided design of lipid nanoparticles for pulmonary gene therapy. Nat. Biotechnol. DOI: 10.1038/s41587-024-02490-y |
| [106] | Maharjan R., Kim K. H., Lee K., et al. (2024). Machine learning-driven optimization of mRNA-lipid nanoparticle vaccine quality with XGBoost/Bayesian method and ensemble model approaches. Journal of Pharmaceutical Analysis.100996. DOI: 10.1016/j.jpha.2024.100996 |
| [107] | Gao S., Fang A., Huang Y., et al. (2024). Empowering biomedical discovery with AI agents. Cell 187:6125−6151. DOI:10.1016/j.cell.2024.09.022 |
| [108] | Wang G., Liu X., Wang K., et al. (2023). Deep-learning-enabled protein-protein interaction analysis for prediction of SARS-CoV-2 infectivity and variant evolution. Nat. Med. 29:2007−2018. DOI:10.1038/s41591-023-02483-5 |
| [109] | Liu T., Shi K. and Li W. (2020). Deep learning methods improve linear B-cell epitope prediction. BioData Min. 13:1. DOI:10.1186/s13040-020-00211-0 |
| [110] | Han W., Chen N., Xu X., et al. (2023). Predicting the antigenic evolution of SARS-COV-2 with deep learning. Nat. Commun. 14:3478. DOI:10.1038/s41467-023-39199-6 |
| [111] | Shen L.-C., Zhang Y., Wang Z., et al. (2025). Self-iterative multiple-instance learning enables the prediction of CD4+T cell immunogenic epitopes. Nat. Mach. Intell. DOI: 10.1038/s42256-025-01073-z |
| [112] | Zhao Y., Yu J., Su Y., et al. (2025). A unified deep framework for peptide–major histocompatibility complex–T cell receptor binding prediction. Nat. Mach. Intell. 7:650−660. DOI:10.1038/s42256-025-01002-0 |
| [113] | Kalemati M., Darvishi S. and Koohi S. (2023). CapsNet-MHC predicts peptide-MHC class I binding based on capsule neural networks. Commun. Biology. 6:492. DOI:10.1038/s42003-023-04867-2 |
| [114] | Chang T. G., Cao Y., Sfreddo H. J., et al. (2024). LORIS robustly predicts patient outcomes with immune checkpoint blockade therapy using common clinical, pathologic and genomic features. Nat. Cancer 5:1158−1175. DOI:10.1038/s43018-024-00772-7 |
| [115] | Jiang Y., Zhang Z., Wang W., et al. (2023). Biology-guided deep learning predicts prognosis and cancer immunotherapy response. Nat. Commun. 14:5135. DOI:10.1038/s41467-023-40890-x |
| [116] | Ma J., Wang S., Zhao C., et al. (2023). Computer-aided discovery of potent broad-spectrum vaccine adjuvants. Angew. Chem. Int. Ed. 62:e202301059. DOI:10.1002/anie.202301059 |
| [117] | Adams C. S., Kim H., Burtner A. E., et al. (2025). De novo design of protein minibinder agonists of TLR3. Nat. Commun. 16:1234. DOI:10.1038/s41467-025-56369-w |
| [118] | Cui Z., Shi C., An R., et al. (2025). In silico-guided discovery of polysaccharide derivatives as adjuvants in nanoparticle vaccines for cancer immunotherapy. ACS Nano 19:2099−2116. DOI:10.1021/acsnano.4c08898 |
| [119] | Mout R., Jing R., Tanaka-Yano M., et al. (2025). Design of soluble Notch agonists that drive T cell development and boost immunity. Cell DOI: 10.1016/j.cell.2025.07.009 |
| [120] | Cross J. L., Choma M. A. and Onofrey J. A. (2024). Bias in medical AI: Implications for clinical decision-making. PLOS Digit. Health. 3:e0000651. DOI:10.1371/journal.pdig.0000651 |
| Zhao F. and Zai X. (2025). Proceeding artificial intelligence technology towards vaccine designs. The Innovation Life 3:100162. https://doi.org/10.59717/j.xinn-life.2025.100162 |
To request copyright permission to republish or share portions of our works, please visit Copyright Clearance Center's (CCC) Marketplace website at marketplace.copyright.com.
Brief introduction to vaccines and AI
AI-assisted protein subunit vaccine design
AI-assisted viral vectored vaccine innovation
AI-based mRNA vaccine modification
Concerns behind AI-driven vaccine development and proposed suggestions